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

Content Provider	IET Digital Library
Author	Alam, Md. Shariful Jassim, Wissam A. Zilany, Muhammad S. A.
Abstract	Classification of speech phonemes is challenging, especially under noisy environments, and hence traditional speech recognition systems do not perform well in the presence of noise. Unlike traditional methods in which features are mostly extracted from the properties of the acoustic signal, this study proposes a new feature for phoneme classification using neural responses from a physiologically based computational model of the auditory periphery. The two-dimensional neurogram was constructed from the simulated responses of auditory-nerve fibres to speech phonemes. Features of neurogram images were extracted using the Discrete Radon Transform, and the dimensionality of features was reduced using an efficient feature selection technique. A standard classifier, Support Vector Machine, was employed to model and test the phoneme classes. Classification performance was evaluated in quiet and under noisy conditions in which test data were corrupted with various environmental distortions such as additive noise, room reverberation, and telephone-channel noise. Performances were also compared with the results from existing methods such as the Mel-frequency cepstral coefficient, Gammatone frequency cepstral coefficient, and frequency-domain linear prediction-based phoneme classification methods. In general, the proposed neural feature exhibited a better classification accuracy in quiet and under noisy conditions compared with the performance of most existing acoustic-signal-based methods.
Starting Page	260
Ending Page	268
Page Count	9
ISSN	17519675
Volume Number	12
e-ISSN	17519683
Issue Number	Issue 3, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/12/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2017.0170
Journal	IET Signal Processing
Publisher Date	2017-10-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Additive Noise Auditory Neurograms Auditory-nerve Fibres Baseline-feature-based Method Feature Extraction Feature Selection Technique Frequency-domain Linear Prediction-based Phoneme Classification Method Gammatone Frequency Cepstral Coefficient Integral Transforms Mel-frequency Cepstral Coefficient Phoneme Classification Radon Transform Radon Transforms Room Reverberation Speech Phonemes Classification Speech Processing Technique Speech Recognition Speech Recognition And Synthesis Speech Recognition System Standard Discriminative Classifier Support Vector Machine Telephone-channel Noise Two-dimensional Neurogram
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

Dynamic Features in the Linear-Logarithmic Hybrid Domain for Automatic Speech Recognition in a Reverberant Environment

Admissible wavelet packet sub-band-based harmonic energy features for Hindi phoneme recognition

Gammatone Cepstral Coefficients: Biologically Inspired Features for Non-Speech Audio Classification

Multi-objective optimisation of wavelet features for phoneme recognition

Unified Training of Feature Extractor and HMM Classifier for Speech Recognition

Non-intrusive speech quality assessment using multi-resolution auditory model features for degraded narrowband speech

Feature selection based on MBFOA for audio signal classification under consideration of Gaussian white noise

A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition

Comparative study of automatic speech recognition techniques

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

Similar Documents

Dynamic Features in the Linear-Logarithmic Hybrid Domain for Automatic Speech Recognition in a Reverberant Environment

Admissible wavelet packet sub-band-based harmonic energy features for Hindi phoneme recognition

Gammatone Cepstral Coefficients: Biologically Inspired Features for Non-Speech Audio Classification

Multi-objective optimisation of wavelet features for phoneme recognition

Unified Training of Feature Extractor and HMM Classifier for Speech Recognition

Non-intrusive speech quality assessment using multi-resolution auditory model features for degraded narrowband speech

Feature selection based on MBFOA for audio signal classification under consideration of Gaussian white noise

A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition

Comparative study of automatic speech recognition techniques

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