Japanese phonetic feature extraction for automatic speech recognition

Banik, Manoj; Eity, Qamrun Nahar; Lisa, Nusrat Jahan; Hassan, Foyzul; Saha, Aloke Kumar; Huda, Mohammad Nurul

doi:10.1109/icsip.2010.5697458

Cited by 2 publications

(1 citation statement)

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“…Hidden Markov Model provides efficient recognition over large vocabulary for continuous speech recognition [2] to speaker dependent as well as speaker independent signals. In this paper two main models of recognition; feature extraction [3] and feature matching have been described. The most important segmentation of constant time framing [4] for increasing efficiency on the cost of phoneme length and more importantly complexity of the phoneme (an important feature of multilingual communications) have been used in our work.…”

Section: Introductionmentioning

confidence: 99%

Adaptive HMM based Speech Recognition to Recognize Multi-lingual Sentence

Gupta¹,

Shabeeb²,

Singh³

et al. 2015

IJCA

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Hidden Markov Models (HMMs) provides an effective framework for the modeling of time-varying sequence of spectral vector. An approach of mapping signal to discrete signal is to define it as a set of acoustic featured symbol over a minimal but constant time interval. The aim of proposing this paper is to recognize the speech sample using hidden markov model (HMM) with the use of cepstrum feature of our given speech sample within an adaptive interval of time for which pitch period is determined and divides the sample in accordance with this period. Secondly the phonetics or exact "pronunciation" of the word needs to be defined. These are established by associated rule probability where probability is done on word"s pronunciation.

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Section: Introductionmentioning

confidence: 99%