Robust Speech Processing using Observation Uncertainty and Uncertainty Propagation: Difference between revisions

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We are pleased to announce a special session at [http://interspeech2015.org/ Interspeech 2015] in Dresden focusing on Observation Uncertainty (OU) and Uncertainty Propagation (UP) research in speech processing.
We are pleased to announce a special session at [http://interspeech2015.org/ Interspeech 2015] in Dresden focusing on Observation Uncertainty (OU) and Uncertainty Propagation (UP) research in speech processing.


Despite its healthy status, OU/UP research in speech processing is currently fragmented across various disciplines and application domains. Consequently, research papers on uncertainty have to compete with other techniques in e.g. ASR or Speaker Identification tracks. This generally discourages the proposal of novel or bold ideas pertaining to the topic. This is particularly relevant in the current environment in which the strong technology transfer and real-data-rich problems force disciplines to adapt themselves more quickly. In particular, although investigations on how to port OU/UP techniques to Deep Neural Networks (DNNs) exist, this area is still yet to be studied. Given the disruptive character of DNNs and the fact that they are not amenable to classic adaptation techniques, this poses a very interesting application dimension for OU/UP techniques. For these reasons, we have proposed a special session devoted to uncertainty as an opportunity for researchers of different sub-domains of speech processing to share ideas, explore new application domains as e.g. paralinguistics, or text based uncertainties and consolidate OU/UP research in DNNs.  
Despite its healthy status, OU/UP research in speech processing is currently fragmented across various disciplines and application domains. Consequently, research papers on uncertainty have to compete with other techniques in e.g. ASR or Speaker Identification tracks. This generally discourages the proposal of novel or bold ideas pertaining to the topic. This is particularly relevant in the current environment in which the strong technology transfer and real-data-rich problems force disciplines to adapt themselves more quickly. In particular, although investigations on how to port OU/UP techniques to Deep Neural Networks (DNNs) exist, this area is still yet to be studied. Given the disruptive character of DNNs and the fact that they are not amenable to classic adaptation techniques, this poses a very interesting application dimension for OU/UP techniques. For these reasons, we have proposed a special session devoted to uncertainty as an opportunity for researchers of different sub-domains of speech processing to share ideas, explore new application domains as e.g. paralinguistics, or text-based uncertainties and consolidate OU/UP research in DNNs.  


The session would cover  
The session would cover  

Revision as of 17:30, 7 January 2015

Call for Papers for a Special Session at Interspeech 2015 *Deadline March 20*

We are pleased to announce a special session at Interspeech 2015 in Dresden focusing on Observation Uncertainty (OU) and Uncertainty Propagation (UP) research in speech processing.

Despite its healthy status, OU/UP research in speech processing is currently fragmented across various disciplines and application domains. Consequently, research papers on uncertainty have to compete with other techniques in e.g. ASR or Speaker Identification tracks. This generally discourages the proposal of novel or bold ideas pertaining to the topic. This is particularly relevant in the current environment in which the strong technology transfer and real-data-rich problems force disciplines to adapt themselves more quickly. In particular, although investigations on how to port OU/UP techniques to Deep Neural Networks (DNNs) exist, this area is still yet to be studied. Given the disruptive character of DNNs and the fact that they are not amenable to classic adaptation techniques, this poses a very interesting application dimension for OU/UP techniques. For these reasons, we have proposed a special session devoted to uncertainty as an opportunity for researchers of different sub-domains of speech processing to share ideas, explore new application domains as e.g. paralinguistics, or text-based uncertainties and consolidate OU/UP research in DNNs.

The session would cover

  • Methods exploiting observation and parameter uncertainty as well as uncertainty propagation for robust speech processing e.g. for inference, learning or adaptation
  • Especially welcome are papers on the topic deep learning and uncertainty. We are confident this is the right moment to tackle this issue and already expect works on the topic
  • All sub-fields are wellcome. Not only ASR and speaker recognition communities but also exploratory works on e.g. paralinguistics or transcription level uncertainties (lattices).

In order to motivate exploratory ideas, and facilitate research we will provide the following tools to those interested in participating:

  • Feature extractors for different types of uncertainties (Wiener posterior, sparsity assumption, Supergaussian) compatible with HTK and Kaldi in Matlab
  • Patches for uncertainty decoding and modified imputation in HTK, thus allowing to use uncertainties in the existing recipes.
  • Patches to read Matlab produced DNN posteriors with Kaldi, thus allowing to implement uncertainty techniques based on DNNs in Matlab.

The use of these tools is *not* obligatory. They are just provided for those interested.

Do not hesitate to contact us if you have any question. All updates on the session progress and available tools will be reported on this website.

Ramón Fernandez Astudillo (ramon.astudillo@inesc-id.pt)

Shinji Watanabe (watanabe@merl.com)

Ahmed Hussen AbdelAziz (Ahmed.HussenAbdelAziz@rub.de)

Dorothea Kolossa (dorothea.kolossa@rub.de)