Phoneme Probability Presentation of Continuous Speech based on Phoneme Spotting

This paper describes a new presentation of continuous speech in terms of the probability of all phoneme types as a function of time. The presentation is called a phoneme probability presentation (PPP) and can be used for phoneme recognition of continuous speech. As a technique to produce the PPP, we have employed hidden Markov models (HMM) with time duration information. This information is essential to spot the phonemes and to produce the PPP. With this information the HMMs of all the phoneme types can compute their probability in parallel and in time synchronism. The PPP can serve as phoneme filters which can produce phoneme probability from continuous speech

Effect of Time Duration and Intrinsic Features for English Phoneme Recognition

This paper describes methods to improve the performance of English phoneme recognition from linguistic view points. The methods include exploiting time duration information in hidden Markov model (HMM), intrinsic feature space for vowel. The time duration constraint imposed on states of the phoneme HMM can improve its recognition rate significantly for phoneme data in continuous pseech. As intrinsic feature spaces for vowel, formants and the time derivative are employed. They improve the phoneme recognition rate considerably compared with the commonly used LPC cepstral coefficients

3D Human Posture Estimation Using HOG Features of Monocular Images

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