Automatic prosodic events detection using syllable-based acoustic and syntactic features

Automatic prosodic event detection is important for both speech understanding and natural speech synthesis since prosody provides additional information over the short-term segmental features and lexical representation of an utterance. Similar to previous work, this paper focuses on automatic detection of coarse level representation of pitch accents, intonational phrase boundaries (IPB), and break indices. We exploit various classifiers and identify effective feature sets to improve performance of prosodic event detection according to acoustic, lexical, and syntactic evidence. our experiments on the Boston University Radio News Corpus show that the neural network classifier achieves the best performance for modeling acoustic evidence, and that support vector machines are more effective for the lexical and syntactic evidence. The combination of the acoustic and the syntactic models yields 89.8% accent detection accuracy, 93.3% IPB detection accuracy, and 91.1% break index detection accuracy. Compared with previous work, the IPB performance is similar, whereas the results for accent and break index detection are significantly better.