NDLI: Using DTW neural–based MFCC warping to improve emotional speech recognition

Content Provider	Springer Nature Link
Author	Sheikhan, Mansour Gharavian, Davood Ashoftedel, Farhad
Copyright Year	2011
Abstract	In recognition of emotional speech, the performance of automatic speech recognition (ASR) systems is degraded significantly. To improve the recognition rate of ASR systems, we can neutralize the Mel-frequency cepstral coefficients (MFCCs) of emotional speech as the most frequently used features in ASR. In this way, the neutralized MFCCs are used in a hidden Markov model (HMM)-based ASR system that has been trained by nonemotional speech. In this paper, the frequency range that is most affected by emotion is determined, and the frequency warping is applied in the calculation process of MFCCs. This warping is performed in Mel filterbank module and/or discrete cosine transform (DCT) module in the process of MFCCs’ calculation. To determine the warping factor, a combined structure using dynamic time warping (DTW) technique and multi-layer perceptron (MLP) neural network is used. Experimental results show that the recognition rate in anger and happiness emotional states is improved when the warping is performed in each of the mentioned modules when the MFCCs are calculated. Also, when the warping is performed in both the Mel filterbank and the DCT modules, the recognition rate of speech in anger and happiness emotional states is improved by 6.4 and 3.0%, respectively.
Starting Page	1765
Ending Page	1773
Page Count	9
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	21
Issue Number	7
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-05-15
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Emotion Speech recognition Frequency warping Dynamic time warping Neural network Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Emotion Recognition using Dynamic Time Warping Technique for Isolated Words

Using Genetic Algorithm to Improve the Performance of Speech Recognition Based on Artificial Neural Network

STOCHASTIC MODELING BASED SPEECH RECOGNITION USING MFCC

Application of an emotional neural network to facial recognition

Emotion Recognition in Speech Using Neural Networks

Based on Artificial Neural Networks for voice recognition word segment

Neural network based recognition of speech using MFCC features

Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition

Wavelet Transform Speech Recognition Using Vector Quantization, Dynamic Time Warping And Artificial Neural Networks (1994)

Using DTW neural–based MFCC warping to improve emotional speech recognition

Similar Documents

Emotion Recognition using Dynamic Time Warping Technique for Isolated Words

Using Genetic Algorithm to Improve the Performance of Speech Recognition Based on Artificial Neural Network

STOCHASTIC MODELING BASED SPEECH RECOGNITION USING MFCC

Application of an emotional neural network to facial recognition

Emotion Recognition in Speech Using Neural Networks

Based on Artificial Neural Networks for voice recognition word segment

Neural network based recognition of speech using MFCC features

Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition

Wavelet Transform Speech Recognition Using Vector Quantization, Dynamic Time Warping And Artificial Neural Networks (1994)

Using DTW neural–based MFCC warping to improve emotional speech recognition