Syntax based prosody modeling using HMM for Bahasa Indonesia

This paper describes how to generate prosody of Indonesian sentences text to speech synthesis with a small corpus using hidden Markov model (HMM). Corpus contains PoS types and phrase categories types that represent text sentence syntax, as well as the type of pitch curve that represents the pattern of speech intonation. Learning process is carried out to obtain the probability of transition and emissions, where the hidden state input is a type of pitch curve pattern, and the state observation is the type of PoS and the type of syntax phrase categories. Text input sentences are syntactically parsed by chunking parser. HMM to predict the type of pitch curve patterns based on the output of chunking parser. Prosody generation is done by a certain rule that processes text input sentence, PoS type, phrase syntax categories type, and pitch curve pattern predict. Prosody generation using diphone concatenation with Indonesian diphone database.