NDLI: Speech emotion classification using combined neurogram and INTERSPEECH 2010 paralinguistic challenge features

Content Provider	IET Digital Library
Author	Jassim, Wissam A. Paramesran, Raveendran Harte, Naomi
Abstract	Recently, increasing attention has been directed to study and identify the emotional content of a spoken utterance. This study introduces a method to improve emotion classification performance under clean and noisy environments by combining two types of features: the proposed neural-responses-based features and the traditional INTERSPEECH 2010 paralinguistic emotion challenge features. The neural-responses-based features are represented by the responses of a computational model of the auditory system for listeners with normal hearing. The model simulates the responses of an auditory-nerve fibre with a characteristic frequency to a speech signal. The simulated responses of the model are represented by the 2D neurogram (time-frequency representation). The neurogram image is sub-divided into non-overlapped blocks and the averaged value of each block is computed. The neurogram features and the traditional emotion features are combined together to form the feature vector for each speech signal. The features are trained using support vector machines to predict the emotion of speech. The performance of the proposed method is evaluated on two well-known databases: the eNTERFACE and Berlin emotional speech data set. The results show that the proposed method performed better when compared with the classification results obtained using neurogram and INTERSPEECH features separately.
Starting Page	587
Ending Page	595
Page Count	9
ISSN	17519675
Volume Number	11
e-ISSN	17519683
Issue Number	Issue 5, Jul (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/11/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2016.0336
Journal	IET Signal Processing
Publisher Date	2017-02-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Auditory-nerve Fibre Berlin Emotional Speech Data Set Emotion Recognition ENTERFACE Speech Data Set INTERSPEECH 2010 Paralinguistic Challenge Feature Knowledge Engineering Technique Neural-responses-based Feature Neurogram Image Signal Classification Signal Processing And Detection Speech Emotion Classification Speech Processing Technique Speech Recognition Speech Recognition And Synthesis Speech Signal Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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

Radon transform of auditory neurograms: a robust feature set for phoneme classification

Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques

Double sparse learning model for speech emotion recognition

Hilbert–Huang–Hurst-based non-linear acoustic feature vector for emotion classification with stochastic models and learning systems

Learning deep features to recognise speech emotion using merged deep CNN

An optimal two stage feature selection for speech emotion recognition using acoustic features

Emotion recognition from facial expressions using hybrid feature descriptors

Emotion recognition from speech: a review

Source phone identification using sketches of features

Speech emotion classification using combined neurogram and INTERSPEECH 2010 paralinguistic challenge features

Similar Documents

Radon transform of auditory neurograms: a robust feature set for phoneme classification

Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques

Double sparse learning model for speech emotion recognition

Hilbert–Huang–Hurst-based non-linear acoustic feature vector for emotion classification with stochastic models and learning systems

Learning deep features to recognise speech emotion using merged deep CNN

An optimal two stage feature selection for speech emotion recognition using acoustic features

Emotion recognition from facial expressions using hybrid feature descriptors

Emotion recognition from speech: a review

Source phone identification using sketches of features

Speech emotion classification using combined neurogram and INTERSPEECH 2010 paralinguistic challenge features