Large vocabulary continuous speech recognition with context-dependent DBN-HMMS

The context-independent deep belief network (DBN) hidden Markov model (HMM) hybrid architecture has recently achieved promising results for phone recognition. In this work, we propose a context-dependent DBN-HMM system that dramatically outperforms strong Gaussian mixture model (GMM)-HMM baselines on a challenging, large vocabulary, spontaneous speech recognition dataset from the Bing mobile voice search task. Our system achieves absolute sentence accuracy improvements of 5.8% and 9.2% over GMM-HMMs trained using the minimum phone error rate (MPE) and maximum likelihood (ML) criteria, respectively, which translate to relative error reductions of 16.0% and 23.2%.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: George E. Dahl, Dong Yu, Li Deng, Alex Acero
• Keywords: Gaussian Mixture Model, Hidden Markov Model, ICASSP 2011, Minimum Phone Error, Signal Processing