Toward better automatic speech recognition

D. O'Shaughnessy, W. Wang, W. Zhu, V. Barreaud, T. Nagarajan, R. Muralishankar

Abstract


Various model techniques to adapt to various speech environments without modifying the basic automatic speech recognition were developed. Statistical data mapping assumes that speech observations are generated by subsets of mutually related random sources. It is a non-linear approach and has the strength to handle non-time-invariant variations. The warped discrete cosine transform cepstrum (WDCTC) has a better performance in a 5-vowel recognition and speaker identification task.

Keywords


Automation; Cosine transforms; Data acquisition; Speech analysis; Statistical methods; Non-linear approach; Speech environments; Vowel recognition; Warped discrete cosine transform cepstrum (WDCTC)

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