In this paper, we propose a method for generating a pronunciation dictionary—extracting typical pronunciations for each word from speech data uttered by Japanese speakers—as one approach to speech recognition targeting English speech uttered by Japanese speakers whose mother tongue is not English. This method includes three processes: a process in which English phoneme HMMs (Hidden Markov Model...

This paper presents a method of constructing a statistical phonemic segment model (SPSM) for a speech recognition system based on speaker-independent context-independent automatic phonemic segmentation. In our recent research, we proposed the phoneme recognition system using the template matching method with the same segmentation, and confirmed that 5-frame-fixed time sequence of feature vector...

We build an automatic phoneme recognition system based on Bayesian Multivariate Modeling which is a static scheme. Phoneme models were built by using stochastic pattern recognition and acoustic phonetic schemes to recognise phonemes. Since our native language is Kannada, a rich South Indian Language, we have used 15 Kannada phonemes to train and test these models. As Mel – Frequency Cepstral Co...

Speech synthesis by unit selection requires the segmentation of a large single speaker high quality recording. Automatic speech recognition techniques, e.g. Hidden Markov Models (HMM), can be optimised for maximum segmentation accuracy. This paper presents the results of tuning such a phoneme segmentation system. Firstly, using no text transcription, the design of an HMM phoneme recogniser is o...

Hemisphere asymmetry in phoneme perception was analyzed. Three basic mechanisms underlying phoneme perception are proposed. Left temporal lobe would be specialized in: (1) ultrashort auditory (echoic) memory; (2) higher resolution power for some language frequencies; and (3) recognition of rapidly changing and time-dependent auditory signals. An attempt was made to apply some neurophysiological...

 SUST, ICERIE Abstract: Hidden Markov model based various phoneme recognition methods for Bengali language is reviewed. Automatic phoneme recognition for Bengali language using multilayer neural network is reviewed. Usefulness of multilayer neural network over single layer neural network is discussed. Bangla phonetic feature table construction and enhancement for Bengali speech recognition is ...

A multistream phoneme recognition framework is proposed based on forming streams from different spectrotemporal modulations of speech. Phoneme posterior probabilities were estimated from each stream separately and combined at the output level. A statistical model of the final estimated posterior probabilities is used to characterize the system performance. During the operation, the best fusion ...

This study investigates the usefulness of wavelet transforms in phoneme recognition. Both discrete wavelet transforms (DWT) and sampled continuous wavelet transforms (SCWT) are tested. The wavelet transform is used as a part of the front-end processor which extracts feature vectors for a speakerindependent HMM-based phoneme recognizer. The results are evaluated on a portion of TIMIT corpus cons...

We propose a stochastic phoneme space transformation technique that allows the conversion of conditional source phoneme posterior probabilities (conditioned on the acoustics) into target phoneme posterior probabilities. The source and target phonemes can be in any language and phoneme format such as the International Phonetic Alphabet. The novel technique makes use of a Kullback-Leibler diverge...

In this report, we propose a discriminative decoder for the recognition of phoneme sequences, i.e. the identification of the uttered phoneme sequence from a speech recording. This task is solved as a 3 step process: a phoneme classifier first classifies each accoustic frame, then temporal consistency features (TCF) are extracted from the phoneme classifier outputs, and finally a sequence decode...