Shaojin Ding
Speech recognition, Neural network compression
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In this paper, we propose Textual Echo Cancellation (TEC) --- a framework for cancelling the text-to-speech (TTS) playback signal from overlapped speech. Such a system can largely improve speech recognition performance and user experience for intelligent devices such as smart speakers, as the user can talk to the device while the device is still playing the TTS signal responding to the previous query. We implement this system by using a novel sequence-to-sequence model with multi-source attention that takes both the mixture signal and the source text of the TTS playback as inputs, and predicts the enhanced audio. Experiments show that the textual information of the TTS playback signal is critical to the enhancement performance. Besides, the text sequence is much smaller in size compared with the raw acoustic signal of the TTS playback, and can be immediately transmitted to the device and the ASR server even before the playback signal is synthesized. Therefore, our proposed approach effectively reduces Internet communication and latency compared with alternative approaches such as acoustic echo cancellation (AEC).
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In this paper, we propose "personal VAD'', a system to detect the voice activity of a target speaker at the frame level. This system is useful for gating the inputs to a streaming speech recognition system, such that it only triggers for the target user, which helps reduce the computational cost and battery consumption. We achieve this by training a VAD-alike neural network which is conditioned on the target speaker embedding or the speaker verification score. For every frame, personal VAD outputs the scores for three classes: non-speech, target speaker speech, and non-target speaker speech. With our optimal setup, we are able to train a 130KB model that out-performs a baseline system where individually trained standard VAD and speaker recognition network are combined to perform the same task.
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