Flexible speech understanding based on combined key-phrase detection and verification

We propose a novel speech understanding strategy based on combined detection and verification of semantically tagged key-phrases in spontaneous spoken utterances. Key-phrases are defined in a top-down manner so as to constitute semantic slots. Their detection directly leads to robust understanding. A phrase network realizes both a wide coverage and a reasonable constraint for detection. A subword-based verifier is then incorporated to reduce false alarms in detection and attach confidence measures of the detected phrases. This set of phrase confidence measures, when incorporated in a spoken dialogue system, forms a basis for designing intelligent speech interfaces that accept only verified key-phrases and reprompt users to clarify unspecified or unrecognized portions. Several forms of confidence measures based on subword-level tests are investigated. The proposed approach was tested on field data collected from real-world trial applications. The combined detection and verification strategy drastically improves the accuracy in handling out-of-grammar utterances over the conventional decoding approaches while maintaining the performance for in-grammar utterances