NDLI: Recognition of isolated digits using DNN–HMM and harmonic noise model

Content Provider	IET Digital Library
Author	Guerid, Abdelkader Houacine, Amrane
Abstract	Speech recognition is an area that is constantly developing. In this study, the authors present a new system of speech recognition applied to the Arabic language. The system proposed here is based on the harmonic plus noise model (HNM). This model is rather used in speech synthesis tasks and is known for providing excellent speech production quality. Thus, their contribution lies in replacing the conventional mel-frequency cepstrum coefficients (MFCC) parameters with a set of acoustic parameters, extracted through the HNM estimation process. The HNM model allows development of a more adapted processing by distinguishing voiced and unvoiced speech frames and by characterising the harmonic property of speech. As common, their system consists of both training and recognition phases. Deep neural networks and hidden Markov models (DNN–HMM) are used for modelling the voiced frames corresponding to the harmonic part. The DNN model is estimated with static and dynamic parameters. Moreover, the unvoiced frames, which represent the noise part of the HNM, are clustered with an HMM model. The spoken Arabic digits are used to measure the performance of the proposed recognition system and a comparison with the MFCC-based approach is performed.
Starting Page	207
Ending Page	214
Page Count	8
ISSN	17519675
Volume Number	13
e-ISSN	17519683
Issue Number	Issue 2, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5131
Journal	IET Signal Processing
Publisher Date	2018-10-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acoustic Parameter Arabic Language Deep Neural Network DNN Model DNN-HMM Dynamic Parameter Feature Extraction Harmonic Noise Model Harmonic Plus Noise Model Hidden Markov Model HMM Model HNM Estimation Process HNM Model Isolated Digit Recognition Markov Processes MFCC-based Approach Natural Language Processing Neural Computing Technique Neural Nets Signal Denoising Speech Processing Speech Processing Technique Speech Production Quality Speech Recognition Speech Recognition And Synthesis Speech Synthesis Speech Synthesis Task Spoken Arabic Digits Static Parameter Unvoiced Speech Frames Voiced Speech Frames
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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