Phoneme segmentation of speech

Ziółko, Bartosz; Manandhar, Suresh; Wilson, Roland

doi:10.1109/icpr.2006.931

Cited by 19 publications

(11 citation statements)

References 7 publications

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“…Wavelet decomposition is used for phoneme segmentation in [22] [23]. The method consists of eight steps: normalization of the signal, 6-level decomposition by wavelets (the authors suggest the use of Meyer wavelet family), evaluation of sum power samples in all subbands, finding the envelope for the power of each subband, calculation of the first derivative of the power, define and group indexes of candidates for segmentation and calculate the average index of each group.…”

Section: Related Workmentioning

confidence: 99%

Speech and phoneme segmentation under noisy environment through spectrogram image analysis

Costa

Lopes

Mello

et al. 2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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This paper presents a new algorithm for speech segmentation based on image analysis of the spectrogram of the signal. The algorithm works in two loops: the first segments the sound in search for the speech signal. The segmented speech returns to the algorithm for phoneme segmentation. For evaluation, the algorithm was applied to TIMIT speech signals with correct speech segmentation of every tested signal, including signals under real-world noise.

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Section: Related Workmentioning

confidence: 99%

Speech and phoneme segmentation under noisy environment through spectrogram image analysis

Costa

Lopes

Mello

et al. 2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…To increase the accuracy and to minimize the memory usage, the phoneme based ASR was developed [4] [5]. The problem with phoneme based model is that the phonemes are context dependent.…”

Section: A Literature Reviewmentioning

confidence: 99%

Syllable based continuous speech recognizer with varied length maximum likelihood character segmentation

Ganesh¹,

Ravichandran²

2013

2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Speech is the most natural and quick mode of transforming and sharing information. To automate the process of speech production and perception, many researches are carried out for more than five decades. For an Automatic speech Recognition (ASR) of a large or unlimited vocabulary, a recognition unit smaller than word size is necessary. In this paper a new approach for segmenting the input utterance into individual characters is presented. The accuracy of boundary detection of baseline Maximum Likelihood (ML) Algorithm and the proposed algorithm is also compared and discussed.

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“…Many algorithms have been proposed in literature for phoneme segmentation. Phoneme segmentation using Discrete Wavelet Transform (DWT) for polish words is reported in [4] and it is reported that the recognized phoneme boundary doesn't match with the actual phoneme boundary. Speech segmentation using Spectral Transition Measure (STM) and Maximum Likelihood (ML) is explained in [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Phoneme Recognition System Using Binary Feature Vector and Correlation Based Classifier

Manikandan

Venkataramani

Ramprasath

et al. 2010

2010 International Conference on Advances in Computer Engineering

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In this paper, a novel approach for phoneme classification using binary feature vector and correlation based classifier is proposed. The input speech segmentation is carried out using the Average Level Crossing Rate (ALCR) information. A 513-point binary feature vector is generated for each of the phoneme segment detected by ALCR boundaries. The phoneme recognition is based on the uniqueness of the frequency content of each of the phoneme. Instead of using a Hidden Markov Model, Artificial Neural Network or Support Vector Machine based classifier, a simple correlation classifier using the correlation between feature vectors and the set of feature vectors generated with training data is employed. The proposed approach is simpler and requires lesser computational resources when compared with other pattern classification techniques. The performance of proposed phoneme recognition system has been evaluated using real-time speech input and the recognition performance of the proposed phoneme recognition system is satisfactory.

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Phoneme segmentation of speech

Cited by 19 publications

References 7 publications

Speech and phoneme segmentation under noisy environment through spectrogram image analysis

Speech and phoneme segmentation under noisy environment through spectrogram image analysis

Syllable based continuous speech recognizer with varied length maximum likelihood character segmentation

A Novel Phoneme Recognition System Using Binary Feature Vector and Correlation Based Classifier

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