With solid theoretical foundations and numerous potential applications, Blind Signal
Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume …

Gaussian Mixture Models (GMMs) and Radial Basis Function (RBF) networks are two of the
promising neural models for pattern classification. In this laboratory exercise, your task is to …

The mel-frequency cepstral coefficient (MFCC) or perceptual linear prediction (PLP) feature
extraction typically used for automatic speech recognition (ASR) employ several principles …

The growth of cellular telephony combined with recent advances in speech recognition
technology results in sizeable potential opportunities for mobile speech recognition …

In real-life applications, errors in the speech recognition system are mainly due to inefficient
detection of speech segments, unreliable rejection of Out-Of-Vocabulary (OOV) words, and …

Foreign accented speech recognition systems have to deal with the acoustic realization of
sounds produced by non-native speakers that does not always match with native speech …

This report presents the integration of several noise reduction methods into the front-end for
speech recognition developed at IDIAP. The chosen methods are: Spectral Subtraction …

This paper presents an approach that uses articulatory features (AF) derived from spectral
features for telephone-based speaker verification. To minimize the acoustic mismatch …

This paper deals with automatic speech recognition robustness for noisy wireless
communications. We propose several solutions to improve speech recognition over the …

Performance of an automatic speech recognition system drops dramatically in the presence
of background noise unlike the human auditory system which is more adept at noisy speech …