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.

本文探讨了基于深度学习框架下的固定词汇量和无限制口语识别中，序列鉴别式训练的有效性，提出了一种有效的方法，通过引入独立音素网格或非关键词空白符号来构建竞争性假设，相比前面基于深度学习的原始方法，在固定词汇和无限制 KWS 任务中，提高了很大的识别性能。