University of Illinois Urbana-Champaign

Speech Bandwidth Extension Using Articulatory Features

Shin, Dongeek

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https://hdl.handle.net/2142/46538

Description

Title
Speech Bandwidth Extension Using Articulatory Features
Author(s)
Shin, Dongeek
Contributor(s)
Hasegawa-Johnson, Mark
Issue Date
2011-12
Keyword(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.
Type of Resource
text
Language
en
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http://hdl.handle.net/2142/46538

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