Telephone speech multi-keyword spotting using fuzzy search algorithm and prosodic verification

In this paper a fuzzy search algorithm is proposed to deal with the recognition error for telephone speech. Since the prosodic information is a very special and important feature for Mandarin speech, we integrate the prosodic information into keyword verification. For multi-keyword detection, we define a keyword relation and a weighting function for reasonable keyword combinations. In the keyword recognizer, 94 INITIAL and 38 FINAL context-dependent Hidden Markov Models (HMM‘s) are used to construct the phonetic recognizer. For prosodic verification, a total of 175 context-dependent HMM’s and five anti-prosodic HMM‘s are used. In this system, 1275 faculty names and department names are selected as the keywords. Using a test set of 3595 conversional speech utterance from 37 speakers (21 male, 16 female), the proposed fuzzy search algorithm and prosodic verification can reduce the error rate from 17.64% to 11.29% for multiple keywords embedded in non-keyword speech.