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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This paper describes a speaking rate adaptation technique for automatic speech recognition. The technique aims to reduce speaking rate variations by applying temporal warping in front-end processing so that the average phone duration in terms of feature frames remains constant. Speaking rate estimates are given by timing information from unadapted decoding outputs. We implement the proposed continuous frame rate normalization (CFRN) technique on a state-of-the-art speech recognition architecture, and evaluate it on the most recent GALE broadcast transcription tasks. Results show that CFRN gives consistent improvement on all four separate systems and two different languages. In fact, the reported numbers represent the best decoding error rates of the corresponding test sets. It is further shown that the technique is effective without retraining, and adds little overhead to the multi-pass recognition pipeline found in state-of-the-art transcription systems.