University of Illinois Urbana-Champaign

Phonetic Landmark Detection for Automatic Language Identification

Harwath, David

This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.

Permalink

https://hdl.handle.net/2142/46998

Description

Title
Phonetic Landmark Detection for Automatic Language Identification
Author(s)
Harwath, David
Contributor(s)
Hasegawa-Johnson, Mark
Issue Date
2010-05
Keyword(s)
  • language identification
  • language detection
  • phonetic landmark detection
  • speech processing
Abstract
This paper presents a method of augmenting shifted-delta cepstral coefficients (SDCCs) with the classification outputs of an array of support vector machines (SVMs) trained to detect a set of manner and place features on telephone speech. The SVM array allows for broad phoneme classification, and when this information is concatenated with SDCCs to form a hybrid feature vector for each acoustic frame, a set of Gaussian mixture models (GMMs) may be trained to perform automatic language identification ((LID). The NTIMIT telephone band speech corpus was used to train the SVM-based distinctive feature recognizer, while the NIST CallFriend telephone corpus was used for training and testing the rest of the system.
Type of Resource
text
Language
en
Permalink
http://hdl.handle.net/2142/46998

Owning Collections

Senior Theses - Electrical and Computer Engineering PRIMARY
The best of ECE undergraduate research