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- W3151287998 abstract "We present Bifocal RNN-T, a new variant of the Recurrent Neural Network Transducer (RNN-T) architecture designed for improved inference time latency on speech recognition tasks. The architecture enables a dynamic pivot for its runtime compute pathway, namely taking advantage of keyword spotting to select which component of the network to execute for a given audio frame. To accomplish this, we leverage a recurrent cell we call the Bifocal LSTM (BF-LSTM), which we detail in the paper. The architecture is compatible with other optimization strategies such as quantization, sparsification, and applying time-reduction layers, making it especially applicable for deployed, real-time speech recognition settings. We present the architecture and report comparative experimental results on voice-assistant speech recognition tasks. Specifically, we show our proposed Bifocal RNN-T can improve inference cost by 29.1% with matching word error rates and only a minor increase in memory size." @default.
- W3151287998 created "2021-04-13" @default.
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- W3151287998 date "2021-06-06" @default.
- W3151287998 modified "2023-09-25" @default.
- W3151287998 title "Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization" @default.
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- W3151287998 doi "https://doi.org/10.1109/icassp39728.2021.9414652" @default.
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