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Lightweight real-time recurrent models for speech enhancement and automatic speech recognition

Traditional recurrent neural networks (RNNs) encounter difficulty in capturing long-term temporal dependencies. However, lightweight recurrent models for speech enhancement are important to improve noisy speech, while being computationally efficient and able to capture long-term temporal dependencie...

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Main Authors: Dhahbi, Sami, Saleem, Nasir, Gunawan, Teddy Surya, Bourouis, Sami, Ali, Imad, Trigui, Aymen, Algarni, Abeer D
Format: Article
Language:English
English
English
Published: Universidad Internacional de la Rioja 2024
Subjects:
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/113757/1/113757_Lightweight%20real-time%20recurrent%20models.pdf
http://irep.iium.edu.my/113757/2/113757_Lightweight%20real-time%20recurrent%20models_SCOPUS.pdf
http://irep.iium.edu.my/113757/3/113757_Lightweight%20real-time%20recurrent%20models_WOS.pdf
http://irep.iium.edu.my/113757/
https://www.ijimai.org/journal/bibcite/reference/3450
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spelling my.iium.irep.1137572024-08-07T08:50:20Z http://irep.iium.edu.my/113757/ Lightweight real-time recurrent models for speech enhancement and automatic speech recognition Dhahbi, Sami Saleem, Nasir Gunawan, Teddy Surya Bourouis, Sami Ali, Imad Trigui, Aymen Algarni, Abeer D TK7885 Computer engineering Traditional recurrent neural networks (RNNs) encounter difficulty in capturing long-term temporal dependencies. However, lightweight recurrent models for speech enhancement are important to improve noisy speech, while being computationally efficient and able to capture long-term temporal dependencies efficiently. This study proposes a lightweight hourglass-shaped model for speech enhancement (SE) and automatic speech recognition (ASR). Simple recurrent units (SRU) with skip connections are implemented where attention gates are added to the skip connections, highlighting the important features and spectral regions. The model operates without relying on future information that is well-suited for real-time processing. Combined acoustic features and two training objectives are estimated. Experimental evaluations using the short time speech intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and word error rates (WERs) indicate better intelligibility, perceptual quality, and word recognition rates. The composite measures further confirm the performance of residual noise and speech distortion. With the TIMIT database, the proposed model improves the STOI and PESQ by 16.21% and 0.69 (31.1%) whereas with the LibriSpeech database, the model improves STOI by 16.41% and PESQ by 0.71 (32.9%) over the noisy speech. Further, our model outperforms other deep neural networks (DNNs) in seen and unseen conditions. The ASR performance is measured using the Kaldi toolkit and achieves 15.13% WERs in noisy backgrounds. Universidad Internacional de la Rioja 2024-06 Article PeerReviewed application/pdf en http://irep.iium.edu.my/113757/1/113757_Lightweight%20real-time%20recurrent%20models.pdf application/pdf en http://irep.iium.edu.my/113757/2/113757_Lightweight%20real-time%20recurrent%20models_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/113757/3/113757_Lightweight%20real-time%20recurrent%20models_WOS.pdf Dhahbi, Sami and Saleem, Nasir and Gunawan, Teddy Surya and Bourouis, Sami and Ali, Imad and Trigui, Aymen and Algarni, Abeer D (2024) Lightweight real-time recurrent models for speech enhancement and automatic speech recognition. International Journal of Interactive Multimedia and Artificial Intelligence, 8 (6). pp. 74-85. ISSN 1989-1660 https://www.ijimai.org/journal/bibcite/reference/3450 10.9781/ijimai.2024.04.003
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
English
topic TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Dhahbi, Sami
Saleem, Nasir
Gunawan, Teddy Surya
Bourouis, Sami
Ali, Imad
Trigui, Aymen
Algarni, Abeer D
Lightweight real-time recurrent models for speech enhancement and automatic speech recognition
description Traditional recurrent neural networks (RNNs) encounter difficulty in capturing long-term temporal dependencies. However, lightweight recurrent models for speech enhancement are important to improve noisy speech, while being computationally efficient and able to capture long-term temporal dependencies efficiently. This study proposes a lightweight hourglass-shaped model for speech enhancement (SE) and automatic speech recognition (ASR). Simple recurrent units (SRU) with skip connections are implemented where attention gates are added to the skip connections, highlighting the important features and spectral regions. The model operates without relying on future information that is well-suited for real-time processing. Combined acoustic features and two training objectives are estimated. Experimental evaluations using the short time speech intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and word error rates (WERs) indicate better intelligibility, perceptual quality, and word recognition rates. The composite measures further confirm the performance of residual noise and speech distortion. With the TIMIT database, the proposed model improves the STOI and PESQ by 16.21% and 0.69 (31.1%) whereas with the LibriSpeech database, the model improves STOI by 16.41% and PESQ by 0.71 (32.9%) over the noisy speech. Further, our model outperforms other deep neural networks (DNNs) in seen and unseen conditions. The ASR performance is measured using the Kaldi toolkit and achieves 15.13% WERs in noisy backgrounds.
format Article
author Dhahbi, Sami
Saleem, Nasir
Gunawan, Teddy Surya
Bourouis, Sami
Ali, Imad
Trigui, Aymen
Algarni, Abeer D
author_facet Dhahbi, Sami
Saleem, Nasir
Gunawan, Teddy Surya
Bourouis, Sami
Ali, Imad
Trigui, Aymen
Algarni, Abeer D
author_sort Dhahbi, Sami
title Lightweight real-time recurrent models for speech enhancement and automatic speech recognition
title_short Lightweight real-time recurrent models for speech enhancement and automatic speech recognition
title_full Lightweight real-time recurrent models for speech enhancement and automatic speech recognition
title_fullStr Lightweight real-time recurrent models for speech enhancement and automatic speech recognition
title_full_unstemmed Lightweight real-time recurrent models for speech enhancement and automatic speech recognition
title_sort lightweight real-time recurrent models for speech enhancement and automatic speech recognition
publisher Universidad Internacional de la Rioja
publishDate 2024
url http://irep.iium.edu.my/113757/1/113757_Lightweight%20real-time%20recurrent%20models.pdf
http://irep.iium.edu.my/113757/2/113757_Lightweight%20real-time%20recurrent%20models_SCOPUS.pdf
http://irep.iium.edu.my/113757/3/113757_Lightweight%20real-time%20recurrent%20models_WOS.pdf
http://irep.iium.edu.my/113757/
https://www.ijimai.org/journal/bibcite/reference/3450
_version_ 1807048414593024000
score 13.211869

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