Pronunciation Adaptation For Disordered Speech Recognition Using State-Specific Vectors of Phone-Cluster Adaptive Training

Sriranjani, R.; Umesh, S.; Reddy, M. Ramasubba

doi:10.18653/v1/w15-5113

Cited by 6 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sriranjani et al [87] proposed a new approach to identify the pronunciation errors of each speaker with dysarthria, using the state-specific vector (SSV) of the acoustic model trained using phoneme cluster adaptation (Phone-CAT). SSV was a low-dimensional vector estimated for each binding state, with each element in the vector representing the weight of a specific mono-phoneme.…”

Section: Language-lexical Model Of Asr For Dysarthric Speechmentioning

confidence: 99%

A Survey of Automatic Speech Recognition for Dysarthric Speech

Qian,

Xiao

2023

Electronics

View full text Add to dashboard Cite

Dysarthric speech has several pathological characteristics, such as discontinuous pronunciation, uncontrolled volume, slow speech, explosive pronunciation, improper pauses, excessive nasal sounds, and air-flow noise during pronunciation, which differ from healthy speech. Automatic speech recognition (ASR) can be very helpful for speakers with dysarthria. Our research aims to provide a scoping review of ASR for dysarthric speech, covering papers in this field from 1990 to 2022. Our survey found that the development of research studies about the acoustic features and acoustic models of dysarthric speech is nearly synchronous. During the 2010s, deep learning technologies were widely applied to improve the performance of ASR systems. In the era of deep learning, many advanced methods (such as convolutional neural networks, deep neural networks, and recurrent neural networks) are being applied to design acoustic models and lexical and language models for dysarthric-speech-recognition tasks. Deep learning methods are also used to extract acoustic features from dysarthric speech. Additionally, this scoping review found that speaker-dependent problems seriously limit the generalization applicability of the acoustic model. The scarce available speech data cannot satisfy the amount required to train models using big data.

show abstract

Section: Language-lexical Model Of Asr For Dysarthric Speechmentioning

confidence: 99%

A Survey of Automatic Speech Recognition for Dysarthric Speech

Qian,

Xiao

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Their approach reduced world error rate by 5.93% and 13.68% compared to baseline and error correction approaches with context-independent confusion matrices, respectively. Sriranjani, et al [120] proposed to use the state specific vector (SSV) of the acoustic model trained by phoneme cluster adaptation (Phone-CAT) to identify the pronunciation errors of each speaker with dysarthria. SSV is a low-dimensional vector estimated for each binding state, with each element representing the weight of a specific mono-phoneme.…”

Section: Language and Lexical Model Of Asr For Dysarthric Speechmentioning

confidence: 99%

A survey of technologies for automatic Dysarthric speech recognition

Qian,

Xiao,

2023

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

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.

show abstract

“…There are many examples of assistive technology that rely on speech and natural language processing. For instance, sign language translation (Camgoz et al, 2018), pronunciation adaptation for disordered speech (Sriranjani et al, 2015) and synthesised voices for individuals with vocal disabilities (Veaux et al, 2013). Text simplification is an area of natural language processing concerned with the simplification of textual information and is often recognised as having assistive applications.…”

Section: Introductionmentioning

confidence: 99%

On the Ethical Considerations of Text Simplification

Gooding¹

2022

Ninth Workshop on Speech and Language Processing for Assistive Technologies (SLPAT-2022)

View full text Add to dashboard Cite

This paper outlines the ethical implications of text simplification within the framework of assistive systems. We argue that a distinction should be made between the technologies that perform text simplification and the realisation of these in assistive technologies. When using the latter as a motivation for research, it is important that the subsequent ethical implications be carefully considered. We provide guidelines for the framing of text simplification independently of assistive systems, as well as suggesting directions for future research and discussion based on the concerns raised.

show abstract

Pronunciation Adaptation For Disordered Speech Recognition Using State-Specific Vectors of Phone-Cluster Adaptive Training

Cited by 6 publications

References 21 publications

A Survey of Automatic Speech Recognition for Dysarthric Speech

A Survey of Automatic Speech Recognition for Dysarthric Speech

A survey of technologies for automatic Dysarthric speech recognition

On the Ethical Considerations of Text Simplification

Contact Info

Product

Resources

About