Wake Word Detection with Alignment-Free Lattice-Free MMI

Yiming Wang; Hang Lv; Daniel Povey; Lei Xie; Sanjeev Khudanpur

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arXiv · 2005.08347

Wake Word Detection with Alignment-Free Lattice-Free MMI

Yiming Wang·Hang Lv·Daniel Povey·Lei Xie·Sanjeev Khudanpur

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Abstract

Always-on spoken language interfaces, e.g. personal digital assistants, rely on a wake word to start processing spoken input. We present novel methods to train a hybrid DNN/HMM wake word detection system from partially labeled training data, and to use it in on-line applications: (i) we remove the prerequisite of frame-level alignments in the LF-MMI training algorithm, permitting the use of un-transcribed training examples that are annotated only for the presence/absence of the wake word; (ii) we show that the classical keyword/filler model must be supplemented with an explicit non-speech (silence) model for good performance; (iii) we present an FST-based decoder to perform online detection. We evaluate our methods on two real data sets, showing 50%--90% reduction in false rejection rates at pre-specified false alarm rates over the best previously published figures, and re-validate them on a third (large) data set.

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BibTeX RIS

Yiming Wang, Hang Lv, Daniel Povey, Lei Xie, Sanjeev Khudanpur. 2020-05-17. Wake Word Detection with Alignment-Free Lattice-Free MMI. https://arxiv.org/abs/2005.08347

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