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We proposed an analysis-by-synthesis (AbS) frame dropping algorithm for the front end of a distributed speech recognition (DSR) system that preserves rapidly changing frames for being more related to speech perception but discards slowly changing frames for providing little information. When applying DSR over error prone packet-switched networks, speech data will inevitably suffer from frame loss...
In distributed speech recognition applications, variable frame rate (VFR) analysis is a technique that can reduce the channel bandwidth and computation resources. In this method, slowly changing frames that provide little information are abandoned. Rapidly changing frames, on the other hand, that are more related to speech perception are preserved. In this paper, we proposed an analysis-by-synthesis...
In a client-server distributed speech recognition (DSR) application, speech features are extracted and quantized at the client-end, and are sent to a remote back-end server for recognition. Although the bandwidth constrains are mostly eliminated, data packets may be lost over error prone channels. In order to reduce the performance degradation because of frame missing, a frequently used error concealment...
The frame rate of the observation sequence in distributed speech recognition applications may be reduced to suit a resource-limited front-end device. In order to use models trained using full-frame-rate data in the recognition of reduced-frame-rate (RFR) data, we propose a method for adapting the transition probabilities of hidden Markov models (HMMs) to match the frame rate of the observation. Experiments...
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