Analysis of vowel deletion in continuous speech

Abstract

Accurate transcription of the utterances during training is critical for recognition performance. The inherent properties of continuous/spontaneous speech across speakers, such as variation in pronunciation, poorly emphasized or over stressed words/sub-word units can lead to misalignment of the waveform at the sub-word unit level. The misalignment is caused by the deviation of the pronunciation from that defined by the pronunciation lexicon. This leads to insertion/deletion of subword units. This is primarily because the transcription is not specific to utterances. In this paper, an attempt is made to correct the transcription at the sub-word unit level using acoustic cues that are available in the waveform. Using sentence-level transcriptions, the transcription of a word is corrected in terms of the phonemes that make up the word. In particular, it is observed that vowels are either inserted or deleted. To support the proposed argument, mispronunciations in continuous speech are substantiated using signal processing and machine learning tools. An automatic data driven annotator exploiting the inferences drawn from the study is used to correct transcription errors. The results show that corrected pronunciations lead to higher likelihood for train utterances in the TIMIT corpus.