We present an attention shift decoding (ASD) method inspired by human speech recognition. In contrast to the traditional automatic speech recognition (ASR) systems, ASD decodes speech inconsecutively using reliability criteria; the gaps (unreliable speech regions) are decoded with the evidence of islands (reliable speech regions). On the BU Radio News Corpus, ASD provides significant improvement (2.9% absolute) over the baseline ASR results when it is used with oracle island-gap information. At the core of the ASD method is the automatic island-gap detection. Here, we propose a new feature set for automatic island-gap detection which achieves 83.7% accuracy. To cope with the imperfect nature of the island-gap classification, we also propose a new ASD algorithm using soft decision. The ASD with soft decision provides 0.4% absolute (2.2% relative) improvement over the baseline ASR results when it is used with automatically detected islands and gaps.
Bibliographic reference. Kalinli, Ozlem / Narayanan, Shrikanth S. (2009): "Continuous speech recognition using attention shift decoding with soft decision", In INTERSPEECH-2009, 1927-1930.