BioVisualSpeech Engine: Difference between revisions

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== Links ==
== Links ==


[https://www.mdpi.com/2078-2489/11/10/470 MDPI link]
[https://www.mdpi.com/2078-2489/11/10/470 Paper link]
 
[https://drive.google.com/file/d/1m5mn7Zc6uHdDiU-hKOmKrc_JpZyIvbcf/view?usp=sharing Program links (Windows, OSX, Ubuntu, Android and iOS]


== Contact ==
== Contact ==


[[Alberto_Abad_Gareta|Alberto Abad]]
[[Alberto_Abad_Gareta|Alberto Abad]]

Revision as of 17:24, 27 November 2020

Abstract

In order to develop computer tools for speech therapy that reliably classify speech productions, there is a need for speech production corpora that characterize the target population in terms of age, gender, and native language. Apart from including correct speech productions, in order to characterize the target population, the corpora should also include samples from people with speech sound disorders. In addition, the annotation of the data should include information on the correctness of the speech productions. Following these criteria, we collected a corpus that can be used to develop computer tools for speech and language therapy of Portuguese children with sigmatism. The proposed corpus contains European Portuguese children’s word productions in which the words have sibilant consonants. The corpus has productions from 356 children from 5 to 9 years of age. Some important characteristics of this corpus, that are relevant to speech and language therapy and computer science research, are that (1) the corpus includes data from children with speech sound disorders; and (2) the productions were annotated according to the criteria of speech and language pathologists, and have information about the speech production errors. These are relevant features for the development and assessment of speech processing tools for speech therapy of Portuguese children. In addition, as an illustration on how to use the corpus, we present three speech therapy games that use a convolutional neural network sibilants classifier trained with data from this corpus and a word recognition module trained on additional children data and calibrated and evaluated with the collected corpus.

Links

Paper link

Program links (Windows, OSX, Ubuntu, Android and iOS

Contact

Alberto Abad