wav2vec2-based Speech Rating System for Children with Speech Sound Disorder

Yaroslav Getman, Ragheb Al-Ghezi, Ekaterina Voskoboinik, Tamás Grósz, Mikko Kurimo, Giampiero Salvi, Torbjørn Svendsen, Sofia Strömbergsson

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)
181 Downloads (Pure)


Speaking is a fundamental way of communication, developed at a young age. Unfortunately, some children with speech sound disorder struggle to acquire this skill, hindering their ability to communicate efficiently. Speech therapies, which could aid these children in speech acquisition, greatly rely on speech practice trials and accurate feedback about their pronunciations. To enable home therapy and lessen the burden on speech-language pathologists, we need a highly accurate and automatic way of assessing the quality of speech uttered by young children. Our work focuses on exploring the applicability of state-of-the-art self-supervised, deep acoustic models, mainly wav2vec2, for this task. The empirical results highlight that these self-supervised models are superior to traditional approaches and close the gap between machine and human performance.
Original languageEnglish
Title of host publicationProceedings of Interspeech'22
PublisherInternational Speech Communication Association (ISCA)
Number of pages5
Publication statusPublished - 2022
MoE publication typeA4 Article in a conference publication
EventInterspeech - Incheon, Korea, Republic of
Duration: 18 Sept 202222 Sept 2022

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
PublisherInternational Speech Communication Association
ISSN (Print)2308-457X
ISSN (Electronic)1990-9772


Country/TerritoryKorea, Republic of


  • speech assessment
  • goodness of pronunciation
  • children speech
  • ASR
  • wav2vec2


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