By Topic

Isolated word intonation recognition using hidden Markov models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)

A method is described for recognition of intonation patterns based on discrete distribution hidden Markov models (HMMs) and vector quantization techniques. Fundamental frequency and energy features, were used to determine the best combination of feature processing and quantization techniques for recognition of statement, question, command, calling, and continuation intonation patterns in isolated words. A recognition accuracy of 89% was achieved for the best-case speaker- and word-independent performance. Recognition performance of human listeners on a 100-word subset yielded 77% accuracy, compared to 83% using HMMs on the same subset

Published in:

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Date of Conference:

3-6 Apr 1990