Hearing aids have advanced significantly over the past decade, primarily due to the
maturing of digital technology. The next decade should see an even greater number of …

We have combined an echo state network (ESN) with a competitive state machine
framework to create a classification engine called the predictive ESN classifier. We derive …

This paper examines how people communicate with computers using speech. Automatic
speech recognition (ASR) transforms speech into text, while automatic speech synthesis [or …

This paper presents main paradigms of research for feature extraction methods to further
augment the state of art in speaker recognition (SR) which has been recognized extensively …

Changes in spectral characteristics are important cues for discriminating and identifying
speech sounds. These changes can occur over very short time intervals. Computing frames …

A front end for automatic speech recognizers is proposed and evaluated which is based on
a quantitative model of the “effective” peripheral auditory processing. The model simulates …

Mel frequency cepstral coefficients (MFCC) are the most widely used speech features in
automatic speech recognition systems, primarily because the coefficients fit well with the …

Prior research has shown that articulatory information, if extracted properly from the speech
signal, can improve the performance of automatic speech recognition systems. However …

Artificial neural networks have been shown to perform well in automatic speech recognition
(ASR) tasks, although their complexity and excessive computational costs have limited their …

As an important part of our daily life, speech has a great impact on the way people
communicate. The Mel filter bank used in the extraction process of MFCC has a better ability …