Using a DBN to integrate sparse classification and GMM-based ASR

The performance of an HMM-based speech recognizer using MFCCs as input is known to degrade dramatically in noisy conditions. Recently, an exemplar-based noise robust ASR approach, called sparse classification (SC), was introduced. While very successfully at lower SNRs, the performance at high SNRs suffered when compared to HMM-based systems. In this work, we propose to use a Dynamic Bayesian Network (DBN) to implement an HMM-model that uses both MFCCs and phone predictions extracted from the SC system as input. By doing experiments on the AURORA-2 connected digit recognition task, we show that our approach successfully combines the strengths of both systems, resulting in competitive recognition accuracies at both high and low SNRs.

• Type: Journal
• Year: 2010
• Where: INTERSPEECH
• Authors: Yang Sun, Jort F. Gemmeke, Bert Cranen, Louis ten Bosch, Lou Boves
• Keywords: Digit Recognition Task, HMM-based Speech Recognizer, INTERSPEECH 2010, Noise Robust Asr, Signal Processing