Automatic phoneme-level segmentation and transcription of the Filipino speech corpus using split temporal context and hybrid-HMM network architecture - Mendoza, Joseph Christian B.
Automatic Speech Recognition (ASR) systems are very valuable in developing large vocabulary speech corpora which are primarily used in the human-machine communication technology. In this project, the objective is to develop a method for automatic phoneme-level segmentation and transcription of the Filipino Speech Corpus (FSC) developed at the UP DSP Laboratory. Phoneme-level recognition and transcription plays a very important role in speech processing. Phonemes are the basic representation of language models and phoneme posteriors are useful are they contain enough information to distinguish words and are small enough to be stored and analyzed.. Currently, only 11.38% of the FSC has been phonemically transcribed. The remaining parts are segmented either by syllable, by word or by sentence. To effectively use the FSC for speech recognition, the whole corpus must be phonetically transcribed. A Split Temporal Context system was employed by the researchers. The STC system did not recognize correctly enough achieving only 21.5% as its highest recognition rate using 25 files for training. Vowel recognition was also investigated using TRAP vectors achieving 66.28% as its highest recognition rate for vowels. The researchers tried to improve the recognition rate of the ASR developed by Robielos et al in 2005 by exhausting 80% of the total file in the FSC and use them as training files. However, with 188 files used in the training set, the recognition rate dropped to 49.53% with a mixed hand and machine segmented training set and the system with pure machine segmented training set achieved only 51.43% recognition rate. The system with 60 file training set consisting of 25 hand segmented and 35 machine decoded files achieved the highest average recognition rate of 67.0%