Automatic Anomaly Detection for Dysarthria across Two Speech Styles: Read vs Spontaneous Speech

Perceptive evaluation of speech disorders is still the standard method in clinical practice for the diagnosing and the following of the condition progression of patients. Such methods include different tasks such as read speech, spontaneous speech, isolated words, sustained vowels, etc. In this context, automatic speech processing tools have proven pertinence in speech quality evaluation and assistive technology-based applications. Though, a very few studies have investigated the use of automatic tools on spontaneous speech. This paper investigates the behavior of an automatic phone-based anomaly detection system when applied on read and spontaneous French dysarthric speech. The behavior of the automatic tool reveals interesting inter-pathology differences across speech styles.

[1]  G. Docherty,et al.  Phonetic variation in dysarthric speech as a function of sampling task. , 1995, European journal of disorders of communication : the journal of the College of Speech and Language Therapists, London.

[2]  Anja Lowit,et al.  Assessment of Motor Speech Disorders , 2010 .

[3]  P. Green,et al.  Automatic speech recognition and training for severely dysarthric users of assistive technology: The STARDUST project , 2006, Clinical linguistics & phonetics.

[4]  Kathryn M. Yorkston,et al.  Comprehensibility of Dysarthric Speech , 1996 .

[5]  J. Martens,et al.  Speech technology-based assessment of phoneme intelligibility in dysarthria. , 2009, International journal of language & communication disorders.

[6]  Elizabeth C Ward,et al.  Speech production in Parkinson's disease: I. An electropalatographic investigation of tongue‐palate contact patterns , 2006, Clinical linguistics & phonetics.

[7]  Corinne Fredouille,et al.  Automatic speech processing for dysarthria: A study of inter-pathology variability , 2015, ICPhS.

[8]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[9]  Carmichael Jn,et al.  Introducing objective acoustic metrics for the frenchay dysarthria assessment procedure. , 2007 .

[10]  Gary L. Pattee,et al.  Bulbar and speech motor assessment in ALS: Challenges and future directions , 2013, Amyotrophic lateral sclerosis & frontotemporal degeneration.

[11]  John J Sidtis,et al.  Dramatic effects of speech task on motor and linguistic planning in severely dysfluent parkinsonian speech , 2012, Clinical linguistics & phonetics.

[12]  Heidi Christensen,et al.  homeService: Voice-enabled assistive technology in the home using cloud-based automatic speech recognition , 2013, SLPAT.

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  A. Aronson,et al.  Differential diagnostic patterns of dysarthria. , 1969, Journal of speech and hearing research.

[15]  Kristin Rosen,et al.  Automatic speech recognition and a review of its functioning with dysarthric speech , 2000 .

[16]  Jean-Pierre Martens,et al.  Automated Intelligibility Assessment of Pathological Speech Using Phonological Features , 2009, EURASIP J. Adv. Signal Process..

[17]  A. Aronson,et al.  Motor Speech Disorders , 2014 .

[18]  Elizabeth C Ward,et al.  Speech production in Parkinson's disease: II. Acoustic and electropalatographic investigation of sentence, word and segment durations , 2006, Clinical linguistics & phonetics.

[19]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[20]  Corinne Fredouille,et al.  Automatic Detection of Abnormal Zones in Pathological Speech , 2011, ICPhS.

[21]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[22]  Myung Jong Kim,et al.  Automatic Assessment of Dysarthric Speech Intelligibility Based on Selected Phonetic Quality Features , 2012, ICCHP.

[23]  Phil D. Green,et al.  Automatic speech recognition with sparse training data for dysarthric speakers , 2003, INTERSPEECH.

[24]  Heidi Christensen,et al.  Learning speaker-specific pronunciations of disordered speech , 2013, INTERSPEECH.

[25]  Lise Crevier-Buchman,et al.  The TYPALOC Corpus: A Collection of Various Dysarthric Speech Recordings in Read and Spontaneous Styles , 2016, LREC.

[26]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[27]  Guillaume Gravier,et al.  The ESTER phase II evaluation campaign for the rich transcription of French broadcast news , 2005, INTERSPEECH.

[28]  Jason A. Whitfield,et al.  Articulatory-acoustic vowel space: application to clear speech in individuals with Parkinson's disease. , 2014, Journal of communication disorders.

[29]  Frank RudziczAravind The TORGO database of acoustic and articulatory speech from speakers with dysarthria , 2012 .

[30]  R. Guiloff,et al.  Dysarthria in amyotrophic lateral sclerosis: A review , 2010, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.

[31]  Raymond D. Kent,et al.  Parametric quantitative acoustic analysis of conversation produced by speakers with dysarthria and healthy speakers. , 2006, Journal of speech, language, and hearing research : JSLHR.

[32]  Corinne Fredouille,et al.  Automatic Detection of Phone-Based Anomalies in Dysarthric Speech , 2015, ACM Trans. Access. Comput..

[33]  Raymond D. Kent,et al.  Acoustic studies of dysarthric speech: methods, progress, and potential. , 1999, Journal of communication disorders.

[34]  K. Hustad The relationship between listener comprehension and intelligibility scores for speakers with dysarthria. , 2008, Journal of speech, language, and hearing research : JSLHR.

[35]  Daniel Kempler,et al.  Effect of Speech Task on Intelligibility in Dysarthria: A Case Study of Parkinson's Disease , 2002, Brain and Language.