Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2

speech segmentation Related Publications

2 Automatic Segmentation of the Clean Speech Signal

Authors: A. Bouzid, M. A. Ben Messaoud, N. Ellouze

Abstract:

Speech Segmentation is the measure of the change point detection for partitioning an input speech signal into regions each of which accords to only one speaker. In this paper, we apply two features based on multi-scale product (MP) of the clean speech, namely the spectral centroid of MP, and the zero crossings rate of MP. We focus on multi-scale product analysis as an important tool for segmentation extraction. The MP is based on making the product of the speech wavelet transform coefficients (WTC). We have estimated our method on the Keele database. The results show the effectiveness of our method. It indicates that the two features can find word boundaries, and extracted the segments of the clean speech.

Keywords: speech segmentation, zero crossings rate, Multi-scale product, Spectral centroid

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1 Hybrid Modeling Algorithm for Continuous Tamil Speech Recognition

Authors: M. Krishnamoorthi, M. Kalamani, S. Valarmathy

Abstract:

In this paper, Fuzzy C-Means clustering with Expectation Maximization-Gaussian Mixture Model based hybrid modeling algorithm is proposed for Continuous Tamil Speech Recognition. The speech sentences from various speakers are used for training and testing phase and objective measures are between the proposed and existing Continuous Speech Recognition algorithms. From the simulated results, it is observed that the proposed algorithm improves the recognition accuracy and F-measure up to 3% as compared to that of the existing algorithms for the speech signal from various speakers. In addition, it reduces the Word Error Rate, Error Rate and Error up to 4% as compared to that of the existing algorithms. In all aspects, the proposed hybrid modeling for Tamil speech recognition provides the significant improvements for speechto- text conversion in various applications.

Keywords: Clustering, Feature Extraction, CSR, speech segmentation, HMM, EM-GMM

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