STATE-CLUSTERING BASED MULTIPLE DEEP NEURAL NETWORKS MODELING APPROACH FOR SPEECH RECOGNITION

STATE-CLUSTERING BASED MULTIPLE DEEP NEURAL NETWORKS MODELING
APPROACH FOR SPEECH RECOGNITION
ABSTRACT
The hybrid deep neural network (DNN) and hidden Markov model (HMM) has recently
achieved dramatic performance gains in automatic speech recognition (ASR). The DNN-based
acoustic model is very powerful but its learning process is extremely time-consuming. In this
paper, we propose a novel DNN-based acoustic modeling framework for speech recognition,
where the posterior probabilities of HMM states are computed from multiple DNNs (mDNN),
instead of a single large DNN, for the purpose of parallel training towards faster turnaround. In
the proposed mDNN method all tied HMM states are first grouped into several disjoint clusters
based on data-driven methods. Next, several hierarchically structured DNNs are trained
separately in parallel for these clusters using multiple computing units (e.g. GPUs). In decoding,
the posterior probabilities of HMM states can be calculated by combining outputs from multiple
DNNs. In this work, we have shown that the training procedure of the mDNN under popular
criteria, including both frame-level cross-entropy and sequence-level discriminative training, can
be parallelized efficiently to yield significant speedup. The training speedup is mainly attributed
to the fact that multiple DNNs are parallelized over multiple GPUs and each DNN is smaller in
size and trained by only a subset of training data. We have evaluated the proposed mDNN
method on a 64-hour Mandarin transcription task and the 320-hour Switchboard task. Compared
to the conventional DNN, a 4-cluster mDNN model with similar size can yield comparable
recognition performance in Switchboard (only about 2% performance degradation) with a greater
than 7 times speed improvement in CE training and a 2.9 times improvement in sequence
training, when 4 GPUs are used.

STATE-CLUSTERING BASED MULTIPLE DEEP NEURAL NETWORKS MODELING APPROACH FOR SPEECH RECOGNITION

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STATE-CLUSTERING BASED MULTIPLE DEEP NEURAL NETWORKS MODELING APPROACH FOR SPEECH RECOGNITION