Detection of voicing and place of articulation of fricatives with deep learning in a virtual speech and language therapy tutor

Children with fricative distortion errors have to learn how to correctly use the vocal folds, and which place of articulation to use in order to correctly produce the different fricatives. Here we propose a virtual tutor for fricatives distortion correction. This is a virtual tutor for speech and la...

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Detalhes bibliográficos
Autor principal: Anjos, Ivo (author)
Outros Autores: Maxine, Eskenazi (author), Marques, Nuno (author), Grilo, Ana Margarida (author), Guimarães, Isabel (author), Magalhães, João (author), Cavaco, Sofia (author)
Formato: conferenceObject
Idioma:eng
Publicado em: 2022
Assuntos:
Fricatives
Speech and language therapy
Convolutional neural networks
Texto completo:http://hdl.handle.net/10400.26/39944
País:Portugal
Oai:oai:comum.rcaap.pt:10400.26/39944
Descrição
Resumo:Children with fricative distortion errors have to learn how to correctly use the vocal folds, and which place of articulation to use in order to correctly produce the different fricatives. Here we propose a virtual tutor for fricatives distortion correction. This is a virtual tutor for speech and language therapy that helps children understand their fricative production errors and how to correctly use their speech organs. The virtual tutor uses log Mel filter banks and deep learning techniques with spectral-temporal convolutions of the data to classify the fricatives in children’s speech by place of articulation and voicing. It achieves an accuracy of 90:40% for place of articulation and 90:93% for voicing with children’s speech. Furthermore, this paper discusses a multidimensional advanced data analysis of the first layer convolutional kernel filters that validates the usefulness of performing the convolution on the log Mel filter bank.

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