Vector-quantization based mask estimation for missing data automatic speech recognition

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.