Use of Haar wavelet transform based multiple template matching for analyses of speech voice

Authors:
Shinji Karasawa;Hiroshi Sakuraba
Affiliations:
Miyagi National College of Technology;Miyagi National College of Technology
Venue:
EATIS '07 Proceedings of the 2007 Euro American conference on Telematics and information systems
Year:
2007

Citing 2
Cited 0

Statistical Model-Based VAD Algorithm with Wavelet Transform*This paper was presented at the International Conference on Circuits/Systems, Computers and Communications, Jeju, Korea, July 4--7, 2005.

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
On the extraction of the valid speech-sound by the merging algorithm with the discrete wavelet transform

ICCS'03 Proceedings of the 1st international conference on Computational science: PartI

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Abstract

Technologies of wavelet transformation were used in JPEG2000 and those will be available for CODEC. Pivotal reminders for voice recognition were investigated by using multi-resolution of Haar wavelet representation (H-WR). Template of a phoneme differs from that of a syllable. Optimum accuracy of the feature depends on segmentation of template-matching (TM) analyses. 64 components of Haar wavelet coefficients (H-WC) for recognition of a phoneme are able to decrease to 15 components with lower frequency. Here, each set of data begins at peak value in each pitch. Sampling frequency is 10 kHz. The period of segment for a phoneme is 6.4msec. Segmentation of phoneme in speech can be checked by using the fact that ratio (r) between SWC (sum of absolute value of WC in a scale) becomes r=1, at a transition. SWC is available as a constituent in vector quantization for a syllable. Short syllables are decoded by means of 8 pieces of SWC, here the SWC was obtained from a set of data of 1024 pieces on a syllable (sampling frequency is 5 kHz, period of extraction for a syllable is 204.8msec).