8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Vector-Quantization Based Mask Estimation for Missing Data Automatic Speech Recognition

Maarten Van Segbroeck, Hugo Van hamme

Katholieke Universiteit Leuven, Belgium

The application of Missing Data Theory (MDT) has shown to improve the robustness of automatic speech recognition (ASR) systems. A crucial part in a MDT-based recognizer is the computation of the reliability masks from noisy data. To estimate accurate masks in environments with unknown, non-stationary noise statistics only weak assumptions can be made about the noise and we need to rely on a strong model for the speech. In this paper, we present a missing data detector that uses harmonicity in the noisy input signal and a vector quantizer (VQ) to confine speech models to a subspace. The resulting system can deal with additive and convolutional noise and shows promising results on the Aurora4 large vocabulary database.

Full Paper

Bibliographic reference.  Segbroeck, Maarten Van / hamme, Hugo Van (2007): "Vector-quantization based mask estimation for missing data automatic speech recognition", In INTERSPEECH-2007, 910-913.