Combining key-phrase detection and subword-based verification for flexible speech understanding

摘要

A flexible speech understanding framework combining key-phrase detection and verification is presented. Detection of semantically-tagged key-phrases directly leads to robust understanding. In order to select reliable detection and eliminate false alarms, utterance verification technique is incorporated. A phrase verifier combines subword-based likelihood ratios of correct models and anti-subword alternate models. A confidence measure that focuses on mis-matched subwords is proposed and demonstrated as the most effective. The combined strategy drastically improves the semantic accuracy for out-of-grammar utterances, while maintaining the performance for in-grammar samples. We also found that utterance verification applied after grammar-based decoding is not so effective as the proposed detection and verification strategy.