Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Robust Emotion Recognition using Spectral and Prosodic FeaturesRobust Emotion Recognition using Pitch Synchronous and Sub-syllabic Spectral Features

Robust Emotion Recognition using Spectral and Prosodic Features: Robust Emotion Recognition using... [This chapter discusses the use of vocal tract information for recognizing the emotions. Linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are used as the correlates of vocal tract information. In addition to LPCCs and MFCCs, formant related features are also explored in this work for recognizing emotions from speech. Extraction of the above mentioned spectral features is discussed in brief. Further extraction of these features from sub-syllabic regions such as consonants, vowels and consonant-vowel transition regions is discussed. Extraction of spectral features from pitch synchronous analysis is also discussed. Basic philosophy and use of Gaussian mixture models is discussed in this chapter for classifying the emotions. The emotion recognition performance obtained from different vocal tract features is compared. Proposed spectral features are evaluated on Indian and Berlin emotion databases. Performance of Gaussian mixture models in classifying the emotional utterances using vocal tract features is compared with neural network models.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Robust Emotion Recognition using Spectral and Prosodic FeaturesRobust Emotion Recognition using Pitch Synchronous and Sub-syllabic Spectral Features

Download PDF
30 pages

Loading next page...
 
/lp/springer-journals/robust-emotion-recognition-using-spectral-and-prosodic-features-robust-y00c2ZzLRP
Publisher
Springer New York
Copyright
© The Author(s) 2013
ISBN
978-1-4614-6359-7
Pages
17 –46
DOI
10.1007/978-1-4614-6360-3_2
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter discusses the use of vocal tract information for recognizing the emotions. Linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are used as the correlates of vocal tract information. In addition to LPCCs and MFCCs, formant related features are also explored in this work for recognizing emotions from speech. Extraction of the above mentioned spectral features is discussed in brief. Further extraction of these features from sub-syllabic regions such as consonants, vowels and consonant-vowel transition regions is discussed. Extraction of spectral features from pitch synchronous analysis is also discussed. Basic philosophy and use of Gaussian mixture models is discussed in this chapter for classifying the emotions. The emotion recognition performance obtained from different vocal tract features is compared. Proposed spectral features are evaluated on Indian and Berlin emotion databases. Performance of Gaussian mixture models in classifying the emotional utterances using vocal tract features is compared with neural network models.]

Published: Jan 13, 2013

Keywords: Speech Signal; Emotion Recognition; Vocal Tract; Speech Emotion Recognition; Emotion Recognition System

There are no references for this article.