ESSA - TF - Congressos e eventos científicos (inclui comunicações e posters em atas de conferências/encontros científicos)
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- Detection of voicing and place of articulation of fricatives with deep learning in a virtual speech and language therapy tutorPublication . Anjos, Ivo; Maxine, Eskenazi; Marques, Nuno; Grilo, Ana Margarida; Guimarães, Isabel; Magalhães, João; Cavaco, SofiaChildren 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.
- A model for sibilant distortion detection in childrenPublication . Anjos, Ivo; Grilo, Ana Margarida; Ascensão, Mariana; Guimarães, Isabel; Magalhães, João; Cavaco, SofiaThe distortion of sibilant sounds is a common type of speech sound disorder in European Portuguese speaking children. Speech and language pathologists (SLP) use different types of speech production tasks to assess these distortions. One of these tasks consists of the sustained production of isolated sibilants. Using these sound productions, SLPs usually rely on auditory perceptual evaluation to assess the sibilant distortions. Here we propose to use an isolated sibilant machine learning model to help SLPs assessing these distortions. Our model uses Mel frequency cepstral coefficients of the isolated sibilant phones and it was trained with data from 145 children. The analysis of the false negatives detected by the model can give insight into whether the child has a sibilant production distortion. We were able to confirm that there exist some relation between the model classification results and the distortion assessment of professional SLPs. Approximately 66% of the distortion cases identified by the model are confirmed by an SLP as having some sort of distortion or are perceived as being the production of a different sound.
- A serious mobile game with visual feedback for training sibilant consonantsPublication . Anjos, Ivo; Grilo, Ana Margarida; Ascensão, Mariana; Guimarães, Isabel; Magalhães, João; Cavaco, SofiaAbstract. The distortion of sibilant sounds is a common type of speech sound disorder (SSD) in Portuguese speaking children. Speech and language pathologists (SLP) frequently use the isolated sibilants exercise to assess and treat this type of speech errors. While technological solutions like serious games can help SLPs to motivate the children on doing the exercises repeatedly, there is a lack of such games for this specic exercise. Another important aspect is that given the usual small number of therapy sessions per week, children are not improving at their maximum rate, which is only achieved by more intensive therapy. We propose a serious game for mobile platforms that allows children to practice their isolated sibilants exercises at home to correct sibilant distortions. This will allow children to practice their exercises more frequently, which can lead to faster improvements. The game, which uses an automatic speech recognition (ASR) system to classify the child sibilant productions, is controlled by the child's voice in real time and gives immediate visual feedback to the child about her sibilant productions. In order to keep the computation on the mobile platform as simple as possible, the game has a client-server architecture, in which the external server runs the ASR system. We trained it using raw Mel frequency cepstral coe cients, and we achieved very good results with an accuracy test score of above 91% using support vector machines.
- Sibilant consonants classification with deep neural networksPublication . Anjos, Ivo; Marques, Nuno; Grilo, Ana Margarida; Guimarães, Isabel; Magalhães, João; Cavaco, SofiaAbstract. Many children su ering from speech sound disorders cannot pronounce the sibilant consonants correctly. We have developed a serious game that is controlled by the children's voices in real time and that allows children to practice the European Portuguese sibilant consonants. For this, the game uses a sibilant consonant classi er. Since the game does not require any type of adult supervision, children can practice the production of these sounds more often, which may lead to faster improvements of their speech. Recently, the use of deep neural networks has given considerable improvements in classi cation for a variety of use cases, from image classication to speech and language processing. Here we propose to use deep convolutional neural networks to classify sibilant phonemes of European Portuguese in our serious game for speech and language therapy. We compared the performance of several diferent arti cial neural networks that used Mel frequency cepstral coefcients or log Mel lterbanks. Our best deep learning model achieves classi cation scores of 95:48% using a 2D convolutional model with log Mel lterbanks as input features.
- Sibilant consonants classification with deep neural networksPublication . Anjos, Ivo; Marques, Nuno; Grilo, Ana Margarida; Guimarães, Isabel; Magalhães, João; Cavaco, SofiaAbstract. Many children su ering from speech sound disorders cannot pronounce the sibilant consonants correctly. We have developed a serious game that is controlled by the children's voices in real time and that allows children to practice the European Portuguese sibilant consonants. For this, the game uses a sibilant consonant classi er. Since the game does not require any type of adult supervision, children can practice the production of these sounds more often, which may lead to faster improvements of their speech. Recently, the use of deep neural networks has given considerable improvements in classi cation for a variety of use cases, from image classication to speech and language processing. Here we propose to use deep convolutional neural networks to classify sibilant phonemes of European Portuguese in our serious game for speech and language therapy. We compared the performance of several diferent arti cial neural networks that used Mel frequency cepstral coefcients or log Mel lterbanks. Our best deep learning model achieves classi cation scores of 95:48% using a 2D convolutional model with log Mel lterbanks as input features.
- The BioVisualSpeech european portuguese sibilants corpusPublication . Grilo, Ana Margarida; Guimarães, Isabel; Ascensão, Mariana; Abad, Alberto; Anjos, Ivo; Magalhães, João; Cavaco, SofiaAbstract. The development of reliable speech therapy computer tools that automatically classify speech productions depends on the quality of the speech data set used to train the classi cation algorithms. The data set should characterize the population in terms of age, gender and native language, but it should also have other important properties that characterize the population that is going to use the tool. Thus, apart from including samples from correct speech productions, it should also have samples from people with speech disorders. Also, the annotation of the data should include information on whether the phonemes are correctly or wrongly pronounced. Here, we present a corpus of European Portuguese children's speech data that we are using in the development of speech classi ers for speech therapy tools for Portuguese children. The corpus includes data from children with speech disorders and in which the labelling includes information about the speech production errors. This corpus, which has data from 356 children from 5 to 9 years of age, focuses on the European Portuguese sibilant consonants and can be used to train speech recognition models for tools to assist the detection and therapy of sigmatism.