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

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.KeywordsSpeech SignalEmotion RecognitionVocal TractSpeech Emotion RecognitionEmotion Recognition SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.