  Speech recognition is a sequence prediction problem. Besides employing
various deep learning approaches for framelevel classification, sequence-level
discriminative training has been proved to be indispensable to achieve the
state-of-the-art performance in large vocabulary continuous speech recognition
(LVCSR). However, keyword spotting (KWS), as one of the most common speech
recognition tasks, almost only benefits from frame-level deep learning due to
the difficulty of getting competing sequence hypotheses. The few studies on
sequence discriminative training for KWS are limited for fixed vocabulary or
LVCSR based methods and have not been compared to the state-of-the-art deep
learning based KWS approaches. In this paper, a sequence discriminative
training framework is proposed for both fixed vocabulary and unrestricted
acoustic KWS. Sequence discriminative training for both sequence-level
generative and discriminative models are systematically investigated. By
introducing word-independent phone lattices or non-keyword blank symbols to
construct competing hypotheses, feasible and efficient sequence discriminative
training approaches are proposed for acoustic KWS. Experiments showed that the
proposed approaches obtained consistent and significant improvement in both
fixed vocabulary and unrestricted KWS tasks, compared to previous frame-level
deep learning based acoustic KWS methods.
