Automatic Prediction of Speech Evaluation Metrics for Dysarthric Speech
| Autor: | Imed Laaridh, Christine Meunier, Corinne Fredouille, Waad Ben Kheder |
|---|---|
| Přispěvatelé: | Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Laboratoire Parole et Langage (LPL), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Meunier, Christine |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Speech recognition media_common.quotation_subject speech disorders 02 engineering and technology Intelligibility (communication) [INFO] Computer Science [cs] computer.software_genre 030507 speech-language pathology & audiology 03 medical and health sciences Perception 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] [SHS.LANGUE]Humanities and Social Sciences/Linguistics speech intelligibility media_common Speech Acoustics business.industry Dysarthric speech [SHS.LANGUE] Humanities and Social Sciences/Linguistics automatic speech processing Index Terms: Dysarthria 020201 artificial intelligence & image processing Artificial intelligence 0305 other medical science business computer Natural language processing i-vectors |
| Zdroj: | Interspeech Interspeech, Aug 2017, Stockholm, Sweden INTERSPEECH |
| Popis: | International audience; During the last decades, automatic speech processing systems witnessed an important progress and achieved remarkable reliability. As a result, such technologies have been exploited in new areas and applications including medical practice. In disordered speech evaluation context, perceptual evaluation is still the most common method used in clinical practice for the diagnosing and the following of the condition progression of patients despite its well documented limits (such as subjectivity). In this paper, we propose an automatic approach for the prediction of dysarthric speech evaluation metrics (intelligibility, severity, articulation impairment) based on the representation of the speech acoustics in the total variability subspace based on the i-vectors paradigm. The proposed approach, evaluated on 129 French dysarthric speakers from the DesPhoAPady and VML databases, is proven to be efficient for the modeling of patient's production and capable of detecting the evolution of speech quality. Also, low RMSE and high correlation measures are obtained between automatically predicted metrics and perceptual evaluations. |
| Databáze: | OpenAIRE |
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