10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Continuous Speech Recognition Using Attention Shift Decoding with Soft Decision

Ozlem Kalinli, Shrikanth S. Narayanan

University of Southern California, USA

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.

Full Paper

Bibliographic reference.  Kalinli, Ozlem / Narayanan, Shrikanth S. (2009): "Continuous speech recognition using attention shift decoding with soft decision", In INTERSPEECH-2009, 1927-1930.