![]() ![]() ![]() The ML based techniques do not follow the conventional method based on identification of acoustic properties. Nowadays with lot of data availability, ML based ASR is preferred because of its simplicity over acoustic-phonetic based methods. ![]() The contribution of Machine Learning (ML) based techniques in overcoming these obstructions in automatic phoneme recognition (APR) is remarkable. The dynamic nature of phonemes and several sources of their variability create lots of barriers in accurate identification of phonemes from an acoustic signal. ![]() As a result, the classification and recognition of phonemes are considered as the primary tasks of automatic speech recognition (ASR) systems irrespective of application domain. These facts have made phoneme recognition an attractive proposition in the entire journey of the Automatic Speech Processing (ASP) till date. The total number of phonemes contained in a language is always very few in comparison to the size of the vocabulary supported by the language. Every language has its own set of phonemes, and all possible words can be considered as ordered sequences of phonemes. A phoneme is the smallest perceptually distinct sound unit that can be distinguished among words in a particular language. ![]()
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