Automatic Detection of Speech Sound Disorder in Child Speech Using Posterior-based Speaker Representations
Refereed conference paper presented and published in conference proceedings
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AbstractThis paper presents a macroscopic approach to automatic detection of speech sound disorder (SSD) in child speech. Typically, SSD is manifested by persistent articulation and phonological errors on specific phonemes in the language. The disorder can be detected by focally analyzing the phonemes or the words elicited by the child subject. In the present study, instead of attempting to detect individual phone- and word-level errors, we propose to extract a subject-level representation from a long utterance that is constructed by concatenating multiple test words. The speaker verification approach, and posterior features generated by deep neural network models, are applied to derive various types of holistic representations. A linear classifier is trained to differentiate disordered speech in normal one. On the task of detecting SSD in Cantonese-speaking children, experimental results show that the proposed approach achieves improved detection performance over previous method that requires fusing phone-level detection results. Using articulatory posterior features to derive i-vectors from multiple-word utterances achieves an unweighted average recall of 78.2% and a macro F1 score of 78.0%.
Acceptance Date15/06/2022
All Author(s) ListSi-Ioi Ng, Cymie Wing-Yee Ng, Jiarui Wang, Tan Lee
Name of ConferenceInterspeech
Start Date of Conference18/09/2022
End Date of Conference22/09/2022
Place of ConferenceIncheon
Country/Region of ConferenceSouth Korea
Proceedings TitleProceedings of INTERSPEECH 2022
Year2022
Month9
Place of PublicationKorea
Pages2853 - 2857
LanguagesEnglish-United States
Keywordschild speech, speech sound disorder, speaker representation, articulatory feature, speech attributes