Grapheme-to-Phoneme (G2P) conversion is the task of predicting the pronunciation of a word given its graphemic or written form. It is a highly important part of both automatic speech recognition (ASR) and text-to-speech (TTS) systems. In this paper, we evaluate seven G2P conversion approaches: Adaptive Regularization of Weight Vectors (AROW) based structured learning (S-AROW), Conditional Random Field (CRF), Joint-sequence models (JSM), phrase-based statistical machine translation (PBSMT), Recurrent Neural Network (RNN), Support Vector Machine (SVM) based point-wise classification, Weighted Finite-state Transducers (WFST) on a manually tagged Myanmar phoneme dictionary. The G2P bootstrapping experimental results were measured with both automatic phoneme error rate (PER) calculation and also manual checking in terms of voiced/unvoiced, tones, consonant and vowel errors. The result shows that CRF, PBSMT and WFST approaches are the best performing methods for G2P conversion on Myanmar language.

At ATR, we are collecting and analysing “meetings” data using a table-top sensor device consisting of a small 360-degree camera surrounded by an array of high-quality directional microphones. This equipment provides a stream of information about the audio and visual events of the meeting which is then processed to form a representation of the verbal and non-verbal interpersonal activity, or discourse flow, during the meeting. This paper describes the resulting corpus of speech and video data which is being collected for the abovere search. It currently includes data from 12 monthly sessions, comprising 71 video and 33 audio modules. Collection is continuingmonthly and is scheduled to include another ten sessions.