An Automatic Prosodic Event Detector Using MSD HMMs for Persian Language

Automatic detection of prosodic events in speech such as detecting the boundaries of Accentual Phrases (APs) and Intonational Phrases (IPs) has been an attractive subject in recent years for speech technologists and linguists. Prosodic events are important for spoken language applications such as speech recognition and translation. Also in order to generate natural speech in text to speech synthesizers, the corpus should be tagged with prosodic events. In this paper, we introduce and implement a prosody recognition system that could automatically label prosodic events and their boundaries at the syllable level in Persian language using a Multi-Space Probability Distribution Hidden Markov Model. In order to implement this system we use acoustic features. Experiments show that the detector achieves about 73.5 % accuracy on accentual phrase labeling and 80.08 % accuracy on intonation phrase detection. These accuracies are comparable with automatic labeling results in American English language which has used acoustic features and achieved 73.97% accuracy in syllable level.