Admissible wavelet packet sub‐band based harmonic energy features using ANOVA fusion techniques for Hindi phoneme recognition. Issue 8 (1st October 2016)

• Record Type: Journal Article
• Authors: Biswas, Astik; Sahu, P.K.; Chandra, Mahesh
• Is Part Of: IET signal processing. Volume 10:Issue 8(2016)
• Page Start: 902
• Page End: 911
• Publication Date: 2016-10-01
• Subjects: wavelet transforms -- speech recognition -- statistical analysis -- natural language processing; admissible wavelet packet sub‐band based harmonic energy features -- ANOVA fusion techniques -- Hindi phoneme recognition -- automatic speech recognition -- complex auditory environment -- WP technique -- variance technique -- Hindi database; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2015.0488
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : Nowadays, wavelet packet (WP) based features have been used extensively to maximise the performance of automatic speech recognition in the complex auditory environment. However, wavelet features are less sufficient to represent the voiced speech. Recent researches on WP technique seek for complementary voicing information to overcome this problem. However, considering additional voicing features results in longer dimension and somehow affected the performance for unvoiced speech. This study presents a new analysis of variance technique to incorporate voicing information on WP sub‐band based features without affecting its performance and dimension. It has been noticed that most of the voiced energy lies below 2 kHz. Thus, the proposed technique emphasises the lower sub‐bands for additional voicing information. Harmonic energy features are combined with recently introduced auditory motivated equivalent rectangular bandwidth like 24‐band WP cepstral features to enhance the performance of voiced phoneme recogniser. Primarily, a standard phonetically balanced Hindi database is used to analyse the performance of the proposed technique across a wide range of signal‐to‐noise ratios. Proposed features show a promising result in phoneme recognition experiment without affecting the feature dimension and performance.