Speech Stuttering Detection and Removal Using Deep Neural Networks

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Speech Stuttering Detection and Removal Using Deep Neural Networks. / Rajput, Shaswat; Nersisson, Ruban; Raj, Alex Noel Joseph; Mary Mekala, A.; Frolova, Olga ; Lyakso, Elena.

Proceedings of the 11th International Conference on Computer Engineering and Networks. ed. / Qi Liu; Xiaodong Liu; Bo Chen; Yiming Zhang; Jiansheng Peng. Springer Nature, 2022. p. 443-451 (Lecture Notes in Electrical Engineering; Vol. 808 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Rajput, S, Nersisson, R, Raj, ANJ, Mary Mekala, A, Frolova, O & Lyakso, E 2022, Speech Stuttering Detection and Removal Using Deep Neural Networks. in Q Liu, X Liu, B Chen, Y Zhang & J Peng (eds), Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol. 808 LNEE, Springer Nature, pp. 443-451, 11th International Conference on Computer Engineering and Networks, CENet2021, Hechi, China, 21/10/21. https://doi.org/10.1007/978-981-16-6554-7_50

APA

Rajput, S., Nersisson, R., Raj, A. N. J., Mary Mekala, A., Frolova, O., & Lyakso, E. (2022). Speech Stuttering Detection and Removal Using Deep Neural Networks. In Q. Liu, X. Liu, B. Chen, Y. Zhang, & J. Peng (Eds.), Proceedings of the 11th International Conference on Computer Engineering and Networks (pp. 443-451). (Lecture Notes in Electrical Engineering; Vol. 808 LNEE). Springer Nature. https://doi.org/10.1007/978-981-16-6554-7_50

Vancouver

Rajput S, Nersisson R, Raj ANJ, Mary Mekala A, Frolova O , Lyakso E. Speech Stuttering Detection and Removal Using Deep Neural Networks. In Liu Q, Liu X, Chen B, Zhang Y, Peng J, editors, Proceedings of the 11th International Conference on Computer Engineering and Networks. Springer Nature. 2022. p. 443-451. (Lecture Notes in Electrical Engineering). https://doi.org/10.1007/978-981-16-6554-7_50

Author

Rajput, Shaswat ; Nersisson, Ruban ; Raj, Alex Noel Joseph ; Mary Mekala, A. ; Frolova, Olga ; Lyakso, Elena. / Speech Stuttering Detection and Removal Using Deep Neural Networks. Proceedings of the 11th International Conference on Computer Engineering and Networks. editor / Qi Liu ; Xiaodong Liu ; Bo Chen ; Yiming Zhang ; Jiansheng Peng. Springer Nature, 2022. pp. 443-451 (Lecture Notes in Electrical Engineering).

BibTeX

@inproceedings{37cb3771bb6f4af68a5ca08db3405427,

title = "Speech Stuttering Detection and Removal Using Deep Neural Networks",

abstract = "There are more than 70 million people worldwide who suffer from stuttering problems. This will affect the confidence of public speaking in people who suffer from this issue. To solve this problem many people take therapy sessions but the therapy sessions are a temporary solution, as soon as they leave therapy sessions this problem might arise again. This work aims to use state of the art machine learning algorithms that have improved over the past few years to solve this problem. We have used the dataset from UCLASS archives which provide the data for stuttered speech in.wav format with time-aligned transcriptions. We have tried different algorithms and optimized our model by hyper parameter tuning to maximize the model{\textquoteright}s accuracy. The algorithm is tested on random speech data with low to heavy stuttering from the same dataset, and it is observed that there is significant reduction in the Word Error Rate (WER) for most of the test cases.",

keywords = "Classification, Deep learning, Filtering, Speech stuttering, Word Error Rate",

author = "Shaswat Rajput and Ruban Nersisson and Raj, {Alex Noel Joseph} and {Mary Mekala}, A. and Olga Frolova and Elena Lyakso",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 11th International Conference on Computer Engineering and Networks, CENet2021 ; Conference date: 21-10-2021 Through 25-10-2021",

year = "2022",

doi = "10.1007/978-981-16-6554-7_50",

language = "English",

isbn = "9789811665530",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Nature",

pages = "443--451",

editor = "Qi Liu and Xiaodong Liu and Bo Chen and Yiming Zhang and Jiansheng Peng",

booktitle = "Proceedings of the 11th International Conference on Computer Engineering and Networks",

address = "Germany",

}

RIS

TY - GEN

T1 - Speech Stuttering Detection and Removal Using Deep Neural Networks

AU - Rajput, Shaswat

AU - Nersisson, Ruban

AU - Raj, Alex Noel Joseph

AU - Mary Mekala, A.

AU - Frolova, Olga

AU - Lyakso, Elena

PY - 2022

Y1 - 2022

N2 - There are more than 70 million people worldwide who suffer from stuttering problems. This will affect the confidence of public speaking in people who suffer from this issue. To solve this problem many people take therapy sessions but the therapy sessions are a temporary solution, as soon as they leave therapy sessions this problem might arise again. This work aims to use state of the art machine learning algorithms that have improved over the past few years to solve this problem. We have used the dataset from UCLASS archives which provide the data for stuttered speech in.wav format with time-aligned transcriptions. We have tried different algorithms and optimized our model by hyper parameter tuning to maximize the model’s accuracy. The algorithm is tested on random speech data with low to heavy stuttering from the same dataset, and it is observed that there is significant reduction in the Word Error Rate (WER) for most of the test cases.

AB - There are more than 70 million people worldwide who suffer from stuttering problems. This will affect the confidence of public speaking in people who suffer from this issue. To solve this problem many people take therapy sessions but the therapy sessions are a temporary solution, as soon as they leave therapy sessions this problem might arise again. This work aims to use state of the art machine learning algorithms that have improved over the past few years to solve this problem. We have used the dataset from UCLASS archives which provide the data for stuttered speech in.wav format with time-aligned transcriptions. We have tried different algorithms and optimized our model by hyper parameter tuning to maximize the model’s accuracy. The algorithm is tested on random speech data with low to heavy stuttering from the same dataset, and it is observed that there is significant reduction in the Word Error Rate (WER) for most of the test cases.

KW - Classification

KW - Deep learning

KW - Filtering

KW - Speech stuttering

KW - Word Error Rate

UR - http://www.scopus.com/inward/record.url?scp=85120086451&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/103b8e13-93aa-3dd4-a897-f8518ab16885/

U2 - 10.1007/978-981-16-6554-7_50

DO - 10.1007/978-981-16-6554-7_50

M3 - Conference contribution

AN - SCOPUS:85120086451

SN - 9789811665530

T3 - Lecture Notes in Electrical Engineering

SP - 443

EP - 451

BT - Proceedings of the 11th International Conference on Computer Engineering and Networks

A2 - Liu, Qi

A2 - Liu, Xiaodong

A2 - Chen, Bo

A2 - Zhang, Yiming

A2 - Peng, Jiansheng

PB - Springer Nature

T2 - 11th International Conference on Computer Engineering and Networks, CENet2021

Y2 - 21 October 2021 through 25 October 2021

ER -

ID: 91234345