Semantic cache model driven speech recognition

This paper proposes an improved semantic based cache model: our method boils down to using the first pass of the ASR system, associated to confidence scores and semantic fields, for driving the second pass. In previous papers, we had introduced a Driven Decoding Algorithm (DDA), which allows us to combine speech recognition systems, by guiding the search algorithm of a primary ASR system by the one-best hypothesis of an auxiliary system. We propose a strategy using DDA to drive a semantic cache, according to the confidence measures. The combination between semantic-cache and DDA optimizes the new decoding process, like an unsupervised language model adaptation. Experiments evaluate the proposed method on 8 hours of speech. Results show that semantic-DDA yields significant improvements to the baseline: we obtain a 4% word error rate relative improvement without acoustic adaptation, and 1.9% after adaptation with a 3xRT ASR system.