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Description
Title: | Speech Bandwidth Extension Using Articulatory Features |
Author(s): | Shin, Dongeek |
Contributor(s): | Hasegawa-Johnson, Mark |
Subject(s): | speech processing
speech bandwidth extension articulatory features |
Abstract: | In this thesis, we present a technique for bandwidth extension (BWE) of a narrow-band (0 - 4 kHz) signal using articulatory features. The proposed technique recovers high-band components (4 - 8 kHz) through Gaussian mixture regression (GMR) on both the acoustic and articulatory features from the X-ray Microbeam (XRMB) speech production database. The Gaussian mixture model (GMM) that is based on acoustic and articulatory features is initialized using k-means and iteratively trained using the expectation-maximization (EM) algorithm. BWE experiments were run using data files from different speakers in the XRMB database as train and test data. Time-frequency plots of speech recovered by different training methods are presented in order to show that articulatory trajectories are helpful in characterizing high-frequencied consonants in speech. Finally, we confirm our hypothesis that using GMM with articulation gives better recovery rate is true by performing Student’s t-test on SNR data between original and recovered speech. |
Issue Date: | 2011-12 |
Genre: | Other |
Type: | Text |
Language: | English |
URI: | http://hdl.handle.net/2142/46538 |
Publication Status: | unpublished |
Peer Reviewed: | not peer reviewed |
Date Available in IDEALS: | 2014-01-14 |
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Senior Theses - Electrical and Computer Engineering
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