Exploring AI-based Speaker Dependent Methods in Dysarthric Speech Recognition

Mulfari, Davide; Celesti, Antonio; Villari, Massimo

doi:10.1109/ccgrid54584.2022.00117

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Mulfari, et al [104] exploited a CNN architecture to predict the presence of a reduced number of speech commands within an atypical speech. In their research, they focused on isolated word recognition.…”

Section: Deep Learning Technologies Of Asr For Dysarthric Speechmentioning

confidence: 99%

A survey of technologies for automatic Dysarthric speech recognition

Qian,

Xiao,

2023

J AUDIO SPEECH MUSIC PROC.

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Speakers with dysarthria often struggle to accurately pronounce words and effectively communicate with others. Automatic speech recognition (ASR) is a powerful tool for extracting the content from speakers with dysarthria. However, the narrow concept of ASR typically only covers technologies that process acoustic modality signals. In this paper, we broaden the scope of this concept that the generalized concept of ASR for dysarthric speech. Our survey discussed the systems encompassed acoustic modality processing, articulatory movements processing and audio-visual modality fusion processing in the application of recognizing dysarthric speech. Contrary to previous surveys on dysarthric speech recognition, we have conducted a systematic review of the advancements in this field. In particular, we introduced state-of-the-art technologies to supplement the survey of recent research during the era of multi-modality fusion in dysarthric speech recognition. Our survey found that audio-visual fusion technologies perform better than traditional ASR technologies in the task of dysarthric speech recognition. However, training audio-visual fusion models requires more computing resources, and the available data corpus for dysarthric speech is limited. Despite these challenges, state-of-the-art technologies show promising potential for further improving the accuracy of dysarthric speech recognition in the future.

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Section: Deep Learning Technologies Of Asr For Dysarthric Speechmentioning

confidence: 99%

A survey of technologies for automatic Dysarthric speech recognition

Qian,

Xiao,

2023

J AUDIO SPEECH MUSIC PROC.

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“…A set of novel techniques and recipe configurations were proposed to learn both speech impairment severity and speaker-identity when constructing and personalizing these systems. In contrast, prior researches mainly focused on using speaker-identity only in speaker-dependent data augmentation [7,9,13,14,18,27] and speaker adapted or dependent ASR system development [1,3,4,7,11,13,19,22,23,25,[31][32][33]. Very limited prior researches utilized speech impairment severity information [2,11,25,[34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Confidence Score Based Conformer Speaker Adaptation for Speech Recognition

Xie¹,

Wang²,

Cui³

et al. 2022

Interspeech 2022

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A key challenge in dysarthric speech recognition is the speaker-level diversity attributed to both speaker-identity associated factors such as gender, and speech impairment severity. Most prior researches on addressing this issue focused on using speaker-identity only. To this end, this paper proposes a novel set of techniques to use both severity and speaker-identity in dysarthric speech recognition: a) multitask training incorporating severity prediction error; b) speaker-severity aware auxiliary feature adaptation; and c) structured LHUC transforms separately conditioned on speaker-identity and severity. Experiments conducted on UASpeech suggest incorporating additional speech impairment severity into state-of-the-art hybrid DNN, E2E Conformer and pre-trained Wav2vec 2.0 ASR systems produced statistically significant WER reductions up to 4.78% (14.03% relative). Using the best system the lowest published WER of 17.82% (51.25% on very low intelligibility) was obtained on UASpeech.

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Speaker recognition system of flexible throat microphone using contrastive learning

Zheng

Chen

et al. 2023

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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Exploring AI-based Speaker Dependent Methods in Dysarthric Speech Recognition

Cited by 5 publications

References 23 publications

A survey of technologies for automatic Dysarthric speech recognition

A survey of technologies for automatic Dysarthric speech recognition

Confidence Score Based Conformer Speaker Adaptation for Speech Recognition

Speaker recognition system of flexible throat microphone using contrastive learning

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