Line spectral frequency representation of subbands for speech recognition

On the use of line spectral frequency parameters for speech recognition

The line spectral frequency (LSF) representation has been proposed by Itakura [l] as an alternative linear prediction (LP) parametric representation. In the context of speech coding, it has been shown [2-61 that this representation has better quantization properties than the other LP parametric representations (such as log area ratios and reflection coefficients). The LSF representation is capa...

Spectral maxima representation for robust automatic speech recognition

In the context of automatic speech recognition, the popular Mel Frequency Cepstral Coefficients(MFCC) as features, though perform very well under clean and matched environments, are observed to fail in mismatched conditions.The spectral maxima are often observed to preserve their locations and energies under noisy environments, but are not presented explicitly by the MFCC features. This paper p...

A Gaussian Mixture Model Spectral Representation for Speech Recognition

On frequency averaging for spectral analysis in speech recognition

Many speech recognition systems use logarithmic filter-bank energies or a linear transformation of them to represent the speech signal. Usually, each of those energies is routinely computed as a weighted average of the periodogram samples that lie in the corresponding frequency band. In this work, we attempt to gain an insight into the statistical properties of the frequency-averaged periodogra...

Spectral Enhancement By Tracking Speech Presence Probability In Subbands