https://www.hlt.inesc-id.pt/wiki/index.php?action=history&feed=atom&title=Speech_Recognition 2026-04-15T03:21:20Z Revision history for this page on the wiki MediaWiki 1.41.0 https://www.hlt.inesc-id.pt/wiki/index.php?title=Speech_Recognition&diff=2824&oldid=prev 2006-07-03T18:27:11Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 18:27, 3 July 2006</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[Audio  Pre-Processing|audio pre-processing]] and [[BN Speech Recognition]] and another covering <del style="font-weight: bold; text-decoration: none;">[[</del>BN Language Models<del style="font-weight: bold; text-decoration: none;">]]</del>. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[Audio  Pre-Processing|audio pre-processing]] and [[BN Speech Recognition]] and another covering BN Language Models. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td></tr> </table>

Imt https://www.hlt.inesc-id.pt/wiki/index.php?title=Speech_Recognition&diff=2823&oldid=prev 2006-07-03T18:26:50Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 18:26, 3 July 2006</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[Audio  <del style="font-weight: bold; text-decoration: none;">indexation</del>|<del style="font-weight: bold; text-decoration: none;">BN Audio Indexation</del>]] and [[BN Speech Recognition]] and another covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[Audio  <ins style="font-weight: bold; text-decoration: none;">Pre-Processing</ins>|<ins style="font-weight: bold; text-decoration: none;">audio pre-processing</ins>]] and [[BN Speech Recognition]] and another covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td></tr> </table>

Imt https://www.hlt.inesc-id.pt/wiki/index.php?title=Speech_Recognition&diff=2819&oldid=prev 2006-07-03T18:17:47Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 18:17, 3 July 2006</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[Audio  indexation|BN Audio <del style="font-weight: bold; text-decoration: none;">Indexing</del>]] and [[BN Speech Recognition]] and another covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[Audio  indexation|BN Audio <ins style="font-weight: bold; text-decoration: none;">Indexation</ins>]] and [[BN Speech Recognition]] and another covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td></tr> </table>

Meinedo https://www.hlt.inesc-id.pt/wiki/index.php?title=Speech_Recognition&diff=2818&oldid=prev 2006-07-03T18:16:29Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 18:16, 3 July 2006</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[<del style="font-weight: bold; text-decoration: none;">BN </del>Audio <del style="font-weight: bold; text-decoration: none;">Pre-processing</del>|BN Audio Indexing]] and [[BN Speech Recognition]] and <del style="font-weight: bold; text-decoration: none;">the other </del>covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[Audio <ins style="font-weight: bold; text-decoration: none;"> indexation</ins>|BN Audio Indexing]] and [[BN Speech Recognition]] and <ins style="font-weight: bold; text-decoration: none;">another </ins>covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td></tr> </table>

Meinedo https://www.hlt.inesc-id.pt/wiki/index.php?title=Speech_Recognition&diff=2812&oldid=prev 2006-07-03T18:11:13Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 18:11, 3 July 2006</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[BN Audio Pre-processing]] and [[BN Speech Recognition]] and the other covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[BN Audio Pre-processing<ins style="font-weight: bold; text-decoration: none;">|BN Audio Indexing</ins>]] and [[BN Speech Recognition]] and the other covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td></tr> </table>

Meinedo https://www.hlt.inesc-id.pt/wiki/index.php?title=Speech_Recognition&diff=2805&oldid=prev 2006-07-03T17:10:17Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 17:10, 3 July 2006</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[Audio <del style="font-weight: bold; text-decoration: none;">indexing</del>]<del style="font-weight: bold; text-decoration: none;">BN Audio Indexing</del>] and [[BN Speech Recognition]] and the other covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[<ins style="font-weight: bold; text-decoration: none;">BN </ins>Audio <ins style="font-weight: bold; text-decoration: none;">Pre-processing</ins>]] and [[BN Speech Recognition]] and the other covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td></tr> </table>

Meinedo https://www.hlt.inesc-id.pt/wiki/index.php?title=Speech_Recognition&diff=2804&oldid=prev 2006-07-03T17:09:24Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 17:09, 3 July 2006</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[BN Audio Indexing<del style="font-weight: bold; text-decoration: none;">]Audio indexing</del>] and [[BN Speech Recognition]] and the other covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[<ins style="font-weight: bold; text-decoration: none;">Audio indexing]</ins>BN Audio Indexing] and [[BN Speech Recognition]] and the other covering [[BN Language Models]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td></tr> </table>

Meinedo https://www.hlt.inesc-id.pt/wiki/index.php?title=Speech_Recognition&diff=2803&oldid=prev 2006-07-03T17:08:49Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 17:08, 3 July 2006</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[BN Audio <del style="font-weight: bold; text-decoration: none;">Pre-processing</del>]] and [[BN Speech Recognition]] and the other covering [[BN <del style="font-weight: bold; text-decoration: none;">Topic Segmentation and Topic Detection</del>]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[BN Audio <ins style="font-weight: bold; text-decoration: none;">Indexing</ins>]<ins style="font-weight: bold; text-decoration: none;">Audio indexing</ins>] and [[BN Speech Recognition]] and the other covering [[BN <ins style="font-weight: bold; text-decoration: none;">Language Models</ins>]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td></tr> </table>

Meinedo https://www.hlt.inesc-id.pt/wiki/index.php?title=Speech_Recognition&diff=2785&oldid=prev 2006-07-03T10:29:31Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 10:29, 3 July 2006</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast News (BN) recognition</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[BN Audio Pre-processing]] and [[BN Speech Recognition]] and the other covering [[BN Topic <del style="font-weight: bold; text-decoration: none;">segmentation </del>and Topic Detection]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[BN Audio Pre-processing]] and [[BN Speech Recognition]] and the other covering [[BN Topic <ins style="font-weight: bold; text-decoration: none;">Segmentation </ins>and Topic Detection]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td></tr> </table>

Meinedo https://www.hlt.inesc-id.pt/wiki/index.php?title=Speech_Recognition&diff=2784&oldid=prev 2006-07-03T10:26:25Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 10:26, 3 July 2006</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The most challenging aspects of speech recognition are the ones related to processing speech in widely different domains, spoken in a variety of dialects, and potentially adverse environments, and dealing with the characteristics of spontaneous speech: no punctuation, disfluencies, emotions, and overlapping turns. In this context, L2F’s activities have been recently concentrated in several research strands:</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast <del style="font-weight: bold; text-decoration: none;">news </del>recognition</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>*Broadcast <ins style="font-weight: bold; text-decoration: none;">News (BN) </ins>recognition</div></td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[<del style="font-weight: bold; text-decoration: none;">Broadcast News </del>Audio Pre-processing and Speech Recognition]] and the other covering [[<del style="font-weight: bold; text-decoration: none;">Broadcast News </del>Topic segmentation and Topic Detection]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>:Our work in this area started in the scope of the European project ALERT. There are currently two PhD Theses on this topic. One covering [[<ins style="font-weight: bold; text-decoration: none;">BN </ins>Audio Pre-processing<ins style="font-weight: bold; text-decoration: none;">]] </ins>and <ins style="font-weight: bold; text-decoration: none;">[[BN </ins>Speech Recognition]] and the other covering [[<ins style="font-weight: bold; text-decoration: none;">BN </ins>Topic segmentation and Topic Detection]]. In order to show the developments several prototypes and demos are made. This is the case of a prototype resulting from the ALERT project: [[SSNT - Summarization of Broadcast News Services]].</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Recognition in adverse environments</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:The field of robust speech recognition is relatively new at L2F. We are currently working on speech enhancement techniques using beam forming for a multi-user speaker environment. Our approach has a single array of 64 linearly spaced microphones.</div></td></tr> </table>

Meinedo