Analysis of vowel addition or deletion in Continuous Speech

Abstract

In order to improve the recognition performance, the articulation of the transcription is very important in the process of training. For continuous speech, the essential characteristics of various speakers are pronunciation variation, over focused or inadequately highlighted words can results the waveform misalignment in the sub word unit margin. Because of the deviation in the articulation leads into misalignment when this is compared with articulation dictionary. So the deletion or insertion of the sub word is necessary. This happens because for each expression, the transcription is not precise. This paper presents the corrections in the transcription at the sub word level utilizing sound prompts that are presented in the waveform. The transcription of a word is fixed Utilizing sentence-level transcriptions with reference to the phonemes that create the word. Specifically, it clarifies that vowels are either deleted or inserted. To help the proposed contention, errors in persistent discourse are validated utilizing machine learning and signal processing tools. A programmed information driven annotator abusing the inductions drawn from the examination is utilized to address transcription errors. The outcomes show that rectified pronunciations lead to higher probability for train expressions in the TIMIT corpus.