University of Illinois Urbana-Champaign

Acoustic event, spoken keyword and emotional outburst detection

Xu, Yijia

Content Files
XU-THESIS-2019.pdf

Permalink

https://hdl.handle.net/2142/105158

Description

Title
Acoustic event, spoken keyword and emotional outburst detection
Author(s)
Xu, Yijia
Issue Date
2019-04-03
Director of Research (if dissertation) or Advisor (if thesis)
Hasegawa-Johnson, Mark Allen
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2019-08-23T20:44:38Z
Keyword(s)
  • audio event detection
  • spoken keyword detection
  • emotion detection
  • speech recognition
  • convolutional neural network
  • hidden Markov model
  • phonetic keywork spotter
Abstract
This thesis presents work in research topics of audio detection. It first describes a system for large-scale multi-label acoustic event detection (AED) in YouTube videos. It explores the potential of the state-of-the-art deep learning classifiers for AED, describes both qualitative and quantitative results (Hit@1 is 47.9%) and presents the pre-trained embedding model as a powerful feature extractor to be adapted to new domains with limited data and improve the detection accuracy (Hit@1 is 58.1%). Second, the thesis focuses on the speech acoustic events and the spoken keyword spotting task for speech. It presents a phonetic keyword spotter as a lightweight alternative to full speech recognition (3x faster, with comparable detection rates and that addresses automatic speech recognition problems). It also explores cross-lingual keyword spotting to support low resource languages and finds that the acoustic model is dominant in determining the cross-lingual keyword search performance. Third, the thesis further presents the emotional outburst detection for infant nonspeech acoustic events. It reports on the efforts to manually code child utterances as being of type “laugh,” “cry,” “fuss,” “babble,” and “hiccup” and to develop the algorithms capable of performing the same task automatically.
Graduation Semester
2019-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/105158
Copyright and License Information
Copyright 2019 Yijia Xu

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