A significant performance reduction is often observed in speech recognition when the rate of speech (ROS) is too low or too high. Most of present approaches to addressing the ROS variation focus on the change of speech signals in dynamic properties caused by ROS, and accordingly modify the dynamic model, e.g., the transition probabilities of the hidden Markov model (HMM). However, an abnormal ROS changes not only the dynamic but also the static property of speech signals, and thus can not be compensated for purely by modifying the dynamic model. This paper proposes an ROS learning approach based on deep neural networks (DNN), which involves an ROS feature as the input of the DNN model and so the spectrum distortion caused by ROS can be learned and compensated for. The experimental results show that this approach can deliver better performance for too slow and too fast utterances, demonstrating our conjecture that ROS impacts both the dynamic and the static property of speech. In addition, the proposed approach can be combined with the conventional HMM transition adaptation method, offering additional performance gains.
This paper is related to previous work on ROS compensation, most of which has been mentioned in the introduction. It should be highlighted that the frame rate normalization approach proposed in @cite_11 is similar to our method in the sense that both change the features extraction according to the ROS. The difference is that our method introduces the ROS feature to regularize the acoustic model learning, while the work in @cite_11 changes the frame step size and so is still an implicit way to adjust the dynamic model.
@cite_11
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