More Web Proxy on the site http://driver.im/

research-article

Image Approach to Speech Recognition on CNN

Authors:

Muhammadjon Musaev,

Ilyos Khujayorov,

Mannon OchilovAuthors Info & Claims

ISCSIC 2019: Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control

Article No.: 57, Pages 1 - 6

https://doi.org/10.1145/3386164.3389100

Published: 06 June 2020 Publication History

Abstract

In this paper has been discussed about speech recognition using spectrogram images and deep convolution neural network(CNN) of Uzbek spoken digits. Spectrogram images from speech signal were generated and it were used for deep CNN training. Presented CNN model contains 3 convolution layers and 2 fully connected layers that discriminative features can be divided and estimated of spectrogram images by those layers. In current research period, dataset of Uzbek spoken digits were made and in based on presented CNN model they were trained. Testing results shows that, proposed approach for Uzbek spoken digits classified 100% accuracy.

References

[1]

A. Incze, Henrietta-Bernadette Jancsó, Z. Szilagyi, A. Farkas, C. Sulyok. Bird Sound Recognition Using a Convolutional Neural Network. SISY 2018 - IEEE 16th International Symposium on Intelligent Systems and Informatics, Proceedings. 2018, pp.295--300.

[2]

A.M. Badshah, J. Ahmad, N.Rahim, S.W.Baik. Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network. 2017 International Conference on Platform Technology and Service, PlatCon 2017 - Proceedings.

[3]

Adrian Rosebrock. Deep Learning for Computer Vision with Python Starter Bundle. 1st Edition (1.2.2). PyImageSearch.com. 2017.

[4]

Al-Darkazali Mohammed. Image processing methods to segment speech spectrograms for word level recognition. Doctoral thesis (PhD), University of Sussex. (2017).

[5]

Andrew Ng, Yan Zhang. Speech Recognition Using Deep Learning Algorithms. Published in 2013.

[6]

B.D. Sarma, S.R.M. Prasanna. Acoustic--Phonetic Analysis for Speech Recognition: A Review. IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India). 2018. pp.305--327.

[7]

C. Glackin, J. Wall, G. Chollet, N. Dugan, N. Cannings. Convolutional neural networks for phoneme recognition. ICPRAM 2018 - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods. 2018. pp.190--195.

[8]

D. Polap, M. Woźniak. Image approach to voice recognition. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. 2018. pp.1--7.

[9]

Dennis, J., Tran, H. D., & Li, H. Spectrogram Image Feature for Sound Event Classification in Mismatched Conditions. IEEE Signal Processing Letters, 18(2), 130--133.

[10]

Diederik P. Kingma, Jimmy Lei Ba. ADAM: A Method for stochastic optimization. Published as a conference paper at ICLR 2015.

[11]

Fisher, William M.; Doddington, George R.; Goudie-Marshall, Kathleen M. (1986). The DARPA Speech Recognition Research Database: Specifications and Status. pp. 93--99.

[12]

Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevskiy, Ilya Sutskever, Ruslan R. Salakhutdinov. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15(1):1929--1958. June 2014.

[13]

Gulmezoglu, M.B., et al., A novel approach to isolated word recognition. IEEE Transactions on Speech and Audio Processing, 1999. 7(6): p. 620--628.

[14]

H. R. Hahnloser, R. Sarpeshkar, M. A. Mahowald, R. J. Douglas, & H. S. Seung, (2000). Erratum: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature, 405(6789), 947--951.

[15]

Ibrahim Patel, Dr. Y. Srinivas Rao. Speech recognition using HMM with MFCC-an analysis using frequency Spectral decomposing technique. Signal & Image Processing: An International Journal (SIPIJ) Vol.1, No.2, December 2010.

[16]

J. Ahmad;, M. Fiaz;, S.-i. Kwon;, M. Sodanil;, B. Vo;, and S. W. Baik, "Gender Identification using MFCC for Telephone Applications - A Comparative Study," International Journal of Computer Science and Electronics Engineering, vol. 3, pp. 351- 355, 2015.

[17]

J. Baker, L. Deng, J. Glass, S. Khudanpur, Chin hui Lee, N. Morgan, and D. O'Shaughnessy, "Developments and directions in speech recognition and understanding, part 1," Signal Processing Magazine, IEEE, vol. 26, no. 3, pp. 75--80, may 2009.

[18]

J. Padmanabhan, M.J.J. Premkumar. Machine learning in automatic speech recognition: A survey. IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India).2015. pp. 240--251.

[19]

J. Zhang, S. Xiao, H. Zhang, L. Jiang. Isolated word recognition with audio derivation and CNN. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI. 2018, pp. 336--341.

[20]

Jaron Collis. "Glossary of Deep Learning: Batch Normalization". medium.com. Retrieved 24 April 2018.

[21]

Klára, V., Viktor, I., Krisztina, M.: Voice disorder detection on the basis of continuous speech. In: 5th European Conference of the International Federation for Medical and Biological Engineering. Springer, Berlin (2011).

[22]

Lonce Wyse. Audio Spectrogram Representations for Processing with Convolutional Neural Networks. Published 2017 in ArXiv.org.

[23]

Longhao Yuan, Jianting Cao. Patients' EEG Data Analysis via Spectrogram Image with a Convolution Neural Network. Conference: International Conference on Intelligent Decision Technologies.

[24]

M Ahmadi, N J Bailey, B S Hoyle. Phoneme recognition using speech image (spectrogram). Published in IEEE: Proceedings of Third International Conference on Signal Processing (ICSP'96).

[25]

M.M.Musaev, U.A.Berdanov, M.F.Rahimov, Shukurov K.E, "Parallel algorithms for acoustic processing of speech signals" International Conference on Signal and Image Processing (ICSIP 2016). China during August 13--15.

[26]

Mark Gales, Steve Young. The Application of Hidden Markov Models in Speech Recognition. Foundations and Trends in Signal Processing Vol. 1, No. 3 (2007) 195--304.

Digital Library

[27]

Mohamed O.M. Khelifa, Yahya Mohamed Elhadj, Yousfi Abdellah, Mostafa Belkasmi. Constructing accurate and robust HMM/GMM models for an Arabic speech recognition system. International Journal of Speech Technology. December 2017, Volume 20, Issue 4, pp 937--949.

[28]

Ossama Abdel-Hamid, Abdel-rahman Mohamed, Hui Jiang, Li Deng, Gerald Penn, and Dong Yu. Convolutional Neural Networks for Speech Recognition. IEEE/ACM Transactions on Audio, speech, and language processing, vol. 22, NO. 10, October 2014.

[29]

Q. T. Nguyen et al., "Speech classification using sift features on spectrogram images," Vietnam Journal of Computer Science, vol. 3, no. 4, pp. 247--257, 2016.

Digital Library

[30]

Rekik, S., Guerchi, D., Selouani, S.A., et al.: Speech steganography using wavelet and Fourier transforms. EURASIP J. Audio Speech Music Process. 2012(1), 20 (2012).

[31]

S. Chu, S. Narayanan, and C.-C. J. Kuo, "Environmental sound recognition with time--frequency audio features," IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, pp. 1142--1158, 2009.

Digital Library

[32]

Sergey Ioffe, Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv.org > cs > arXiv:1502.03167.

[33]

Sukmawati Nur Endah, Satriyo Adhy, Sutikno, Rizky Akbar. Automatic Speech Recognition for Indonesian using Linear Predictive Coding (LPC) and Hidden Markov Model (HMM). Proceeding of 5th International Seminar on New Paradigm and Innovation on Natural Science and Its Application (5th ISNPINSA), 7-8 October 2015, Semarang.

[34]

Tungikar, V.V. and J. Mokashi, Study of Hidden Markov Model for Isolated Word Recognition. SYSTEM, 2016. 4(8).

[35]

Venkatesh Boddapati, Andrej Petef, Jim Rasmusson, Lars Lundberg. Classifying environmental sounds using image recognition networks. December 2017. Procedia Computer Science 112:2048--2056.

Digital Library

[36]

Vinod Nair, Geoffrey E, Hinton. Rectified Linear Units Improve Restricted Boltzmann Machines Vinod Nair. Conference: Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21--24, 2010, Haifa, Israel.

[37]

Waibel A. H, Hanazawa T, Hinton G, Shikano K, Lang K. "Phoneme Recognition Using Time-Delay Neural Networks.", IEEE Trans. on ASSP, Vol. ASSP-37, No. 3, March 1989.

[38]

Wang, S., Chen, X., Cai, G., et al.: Matching demodulation transform and synchro squeezing in time-frequency analysis. IEEE Trans. Signal Process. 62(1), 69--84 (2013).

[39]

X. Glorot, A. Bordes, Y. Bengio. Deep Sparse Rectifier Neural Networks. Conference: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS). 2015.

[40]

Yingying Li, Siyuan Pi, Nanfeng Xiao. Speech Recognition Method Based on Spectrogram. Proceedings of the International Conference on Mechatronics and Intelligent Robotics (ICMIR2017) - Volume 1.

Cited By

Tavakoli MChandra RTian FBravo C(2025)Multi-modal deep learning for credit rating prediction using text and numerical data streamsApplied Soft Computing10.1016/j.asoc.2025.112771(112771)Online publication date: Jan-2025
https://doi.org/10.1016/j.asoc.2025.112771
Ayall TZhou CLiu HBrhanemeskel GAbate SAdjeisah M(2024)Amharic spoken digits recognition using convolutional neural networkJournal of Big Data10.1186/s40537-024-00910-z11:1Online publication date: 4-May-2024
https://doi.org/10.1186/s40537-024-00910-z
Wickramasinghe BChowdhury SKosta APonghiran WRoy K(2024)Unlocking the Potential of Spiking Neural Networks: Understanding the What, Why, and WhereIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2023.332974716:5(1648-1663)Online publication date: Oct-2024
https://doi.org/10.1109/TCDS.2023.3329747
Show More Cited By

Index Terms

Image Approach to Speech Recognition on CNN
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis

Recommendations

Deep Learning for Robust Speech Command Recognition Using Convolutional Neural Networks (CNN)
IC3INA '22: Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications

The rapid development of mobile devices has made human-computer interaction through voice increasingly popular and effective. This condition is made possible by the rapid growth of Automatic Speech Recognition (ASR) technologies. ASR can convert human ...
Characterizing and detecting spontaneous speech: Application to speaker role recognition

Processing spontaneous speech is one of the many challenges that automatic speech recognition systems have to deal with. The main characteristics of this kind of speech are disfluencies (filled pause, repetition, false start, etc.) and many studies have ...
Speech-Input Speech-Output Communication for Dysarthric Speakers Using HMM-Based Speech Recognition and Adaptive Synthesis System

Dysarthria is a motor speech disorder that causes inability to control and coordinate one or more articulators. This makes it difficult for a dysarthric speaker to utter certain speech sound units, thereby producing poorly articulated, slurred, and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ISCSIC 2019: Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control

September 2019

397 pages

ISBN:9781450376617

DOI:10.1145/3386164

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ISCSIC 2019

ISCSIC 2019: 2019 3rd International Symposium on Computer Science and Intelligent Control

September 25 - 27, 2019

Amsterdam, Netherlands

Acceptance Rates

ISCSIC 2019 Paper Acceptance Rate 77 of 152 submissions, 51%;

Overall Acceptance Rate 192 of 401 submissions, 48%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
330
Total Downloads

Downloads (Last 12 months)53
Downloads (Last 6 weeks)3

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Tavakoli MChandra RTian FBravo C(2025)Multi-modal deep learning for credit rating prediction using text and numerical data streamsApplied Soft Computing10.1016/j.asoc.2025.112771(112771)Online publication date: Jan-2025
https://doi.org/10.1016/j.asoc.2025.112771
Ayall TZhou CLiu HBrhanemeskel GAbate SAdjeisah M(2024)Amharic spoken digits recognition using convolutional neural networkJournal of Big Data10.1186/s40537-024-00910-z11:1Online publication date: 4-May-2024
https://doi.org/10.1186/s40537-024-00910-z
Wickramasinghe BChowdhury SKosta APonghiran WRoy K(2024)Unlocking the Potential of Spiking Neural Networks: Understanding the What, Why, and WhereIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2023.332974716:5(1648-1663)Online publication date: Oct-2024
https://doi.org/10.1109/TCDS.2023.3329747
Zhang JWang LXu M(2024)Dynamic Attention Fusion Decoder for Speech Recognition2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831705(3125-3132)Online publication date: 6-Oct-2024
https://doi.org/10.1109/SMC54092.2024.10831705
Wangweera CZanini P(2024)Comparison review of image classification techniques for early diagnosis of diabetic retinopathyBiomedical Physics & Engineering Express10.1088/2057-1976/ad726710:6(062001)Online publication date: 5-Sep-2024
https://doi.org/10.1088/2057-1976/ad7267
Zeki UKaranfiller TYurtkan K(2024)Subject dependent speech verification approach for assistive special educationEducation and Information Technologies10.1007/s10639-024-12474-929:13(16157-16175)Online publication date: 7-Feb-2024
https://dl.acm.org/doi/10.1007/s10639-024-12474-9
Din NZhang LYang Y(2023)Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN FeaturesSensors10.3390/s2304192723:4(1927)Online publication date: 8-Feb-2023
https://doi.org/10.3390/s23041927
Mukhamadiyev AKhujayarov ICho J(2023)Voice-Controlled Intelligent Personal Assistant for Call-Center Automation in the Uzbek LanguageElectronics10.3390/electronics1223485012:23(4850)Online publication date: 30-Nov-2023
https://doi.org/10.3390/electronics12234850
Rakhimov MJavliev SNasimov R(2023)Parallel Approaches in Deep Learning: Use Parallel ComputingProceedings of the 7th International Conference on Future Networks and Distributed Systems10.1145/3644713.3644738(192-201)Online publication date: 21-Dec-2023
https://dl.acm.org/doi/10.1145/3644713.3644738
Maclean KTriguero I(2023)Identifying bird species by their calls in SoundscapesApplied Intelligence10.1007/s10489-023-04486-853:19(21485-21499)Online publication date: 20-Mar-2023
https://doi.org/10.1007/s10489-023-04486-8
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten