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Communication Dans Un Congrès Année : 2020

Automatic Prediction of Speech Intelligibility Based on X-Vectors in the Context of Head and Neck Cancer

Résumé

In the context of pathological speech, perceptual evaluation isstill the most widely used method for intelligibility estimation. Despite being considered a staple in clinical settings, it has a well-known subjectivity associated with it, which results ingreater variances and low reproducibility. On the other hand,due to the increasing computing power and latest research, automatic evaluation has become a growing alternative to perceptual assessments. In this paper we investigate an automatic prediction of speech intelligibility using the x-vector paradigm, in the context of head and neck cancer. Experimental evaluation of the proposed model suggests a high correlation rate when applied to our corpus of HNC patients (p= 0.85). Our approach also displayed the possibility of achieving very high correlation values (p= 0.95) when adapting the evaluation to each individual speaker, displaying a significantly more accurate prediction whilst using smaller amounts of data. These results can also provide valuable insight to the redevelopment of test protocols, which typically tend to be substantial and effort-intensive for patients
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Dates et versions

hal-03122735 , version 1 (19-03-2024)

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Citer

Sebastião Quintas, Julie Mauclair, Virginie Woisard, Julien Pinquier. Automatic Prediction of Speech Intelligibility Based on X-Vectors in the Context of Head and Neck Cancer. 21st INTERSPEECH (2020), International Speech Communication Association (ISCA); Institute of Automation of the Chinese Academy of Sciences (CASIA); Chinese University of Hong Kong (CUHK); Tsinghua University; Shanghai Jiao Tong University, Oct 2020, Shangai (fully virtual conference), China. pp.4976--4980, ⟨10.21437/Interspeech.2020-1431⟩. ⟨hal-03122735⟩
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