For past several decades, visual speech signal processing has been an attractive research topic for overcoming certain audio-only recognition problems. In recent years, there have been many automatic speech-reading systems proposed that combine audio and visual speech features. For all such systems, the objective of these audio-visual speech recognizers is to improve recognition accuracy, parti...

This paper presents our studies of automatic speech recognition based on electromyographic biosignals captured from the articulatory muscles in the face using surface electrodes. We develop a phone-based speech recognizer and describe how the performance of this recognizer improves by carefully designing and tailoring the extraction of relevant speech feature toward electromyographic signals. O...

In this paper, we examine robust feature extraction methods for automatic speech recognition (ASR) in noise-distorted environments. Several perceptual experiments have shown that the range between 1 and 10 Hz of modulation frequency band is important for ASR. Combining the coefficients of multi-resolutional Fourier transform to split the important modulation frequency band for ASR into several ...

Feature selection and feature extraction have always been of utmost importance owing to their capability remove redundant irrelevant features, reduce the vector space size, control computational time, improve performance for more accurate classification tasks, especially in text categorization. These engineering techniques can further be optimized using optimization algorithms. This paper propo...

It is common practice to use similar or even the same feature extraction methods for automatic speech recognition and speaker identification. While the front-end for the former requires to preserve phoneme discrimination and to compensate for speaker differences to some extend, the front-end for the latter has to preserve the unique characteristics of individual speakers. It seems, therefore, c...

A novel unsupervised neural network for dimensionality reduction which seeks directions emphasizing multimodality is presented, and its connection to exploratory projection pursuit methods is discussed. This leads to a new statistical insight to the synaptic modification equations governing learning in Bienenstock, Cooper, and Munro (BCM) neurons (1982). The importance of a dimensionality reduc...

Wushour·silamu Caiqin Nuominghua College of information science and engineering Xinjiang University, Urumqi 830046 Abstract: A small vocabulary, isolated word speech recognition system in java has been realized. In this system we have done the extraction of feature parameter, the training of speech model parameter and the recognition of the recorded speech. MFCC is used as feature parameter, HM...

Environmental robustness is an important area of research in speech recognition. Mismatch between trained speech models and actual speech to be recognized is due to factors like background noise. It can cause severe degradation in the accuracy of recognizers which are based on commonly used features like mel-frequency cepstral co-efficient (MFCC) and linear predictive coding (LPC). It is well u...