Improved Malay vowel feature extraction method based on first and second formants

There are many speech recognition applications that use vowels phonemes.Among them are speech therapy systems that improve utterances of word pronunciation especially to children.There are also systems that teach hearing impaired person to speak properly by pronouncing words with a good degree of intelligibility.All of these systems require high degree of vowel recognition capability.This paper presents a new method of Malay vowel feature extraction based on formant and spectrum envelope called First Formant Bandwidth (F1BW).It is an effort to increase Malay vowel recognition capability by using a new speech database that consist of words uttered by Malaysian speakers from the three major races, Malay, Chinese and Indians.Based on single frame analysis, F1BW performs better than MFCC by more than 9% based on four classifiers of Levenberg-Marquart trained Neural Network, K-Nearest Neighbours, Multinomial Logistic Regression and Linear Discriminant Analysis

Noise robustness of first formant bandwidth (F1BW) features in Malay vowel recognition

Applications that use vowel phonemes require a high degree of vowel recognition capability.The performance of speech recognition application under adverse noisy conditions often becomes the topic of interest among speech recognition researchers regardless of the languages in use. In Malaysia, there are an increasing number of speech recognition researchers focusing on developing independent speaker speech recognition systems that use the Malay language which is noise robust and accurate.This paper present a study of noise robust capability of an improved vowel feature extraction method called First Formant Bandwidth (F1BW).The features are extracted from both original data and noise-added data and classified using three classifiers; (i) Multinomial Logistic Regression (MLR), (ii) K-Nearest Neighbors (K-NN) and Linear Discriminant Analysis (LDA).The results show that the proposed F1BW is robust towards noise and LDA performs the best in overall vowel classification compared to MLR and K-NN in terms of robustness capability, especially with signal-to-noise (SNR) above 20dB