A study of adaptive enhancement methods for improved distant speech recognition
Name
1078221576-MIT.pdf
Description
Full printable version
Size
3.14 MB
Format
Adobe PDF
Checksum (MD5)
c76266e00ca6261db1a75c9265d03019
Author(s)
Titus, Andrew Richard
Advisor(s)
James Glass and Hao Tang.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Automatic speech recognition systems trained on speech data recorded by microphones placed close to the speaker tend to perform poorly on speech recorded by microphones placed farther away from the speaker due to reverberation effects and background noise. I designed and implemented a variety of machine learning models to improve distant speech recognition performance by adaptively enhancing incoming speech to appear as if it was recorded in a close-talking environment, regardless of whether it was originally recorded in a close-talking or distant environment. These were evaluated by passing the enhanced speech to acoustic models trained on only close-talking speech and comparing error rates to those achieved without speech enhancement. Experiments conducted on the AMI, TIMIT and TED-LIUM datasets indicate that decreases in error rate on distant speech of up to 33% relative can be achieved by these with only minor increases (1% relative) on clean speech.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 65-68).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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