Acoustic data-driven grapheme-to-phoneme conversion using KL-HMM

This paper proposes a novel grapheme-to-phoneme (G2P) conversion approach where first the probabilistic relation between graphemes and phonemes is captured from acoustic data using Kullback-Leibler divergence based hidden Markov model (KL-HMM) system. Then, through a simple decoding framework the information in this probabilistic relation is integrated with the sequence information in the orthographic transcription of the word to infer the phoneme sequence. One of the main application of the proposed G2P approach is in the area of low linguistic resource based automatic speech recognition or text-to-speech systems. We demonstrate this potential through a simulation study where linguistic resources from one domain is used to create linguistic resources for a different domain.