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Enhanced Intentness Estimation in a Colloquy

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Author(s): M. Nachamai | T. Santhanam | M. Muthuraman

Journal: Information Technology Journal
ISSN 1812-5638

Volume: 7;
Issue: 2;
Start page: 366;
Date: 2008;
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Keywords: Artificial Neural Network (ANN) | generative factor analyzed hidden Markov model (GFA-HMM) | minimum distance classifier (MDC) | principal component analysis (PCA) | linear discriminant analysis (LDA) | MPL

ABSTRACT
This study proposes a methodology to find the interest levels of two speakers in a conversation. The ANN-HMM approach-a hybrid method is adopted. The hybrid method uses language input as an additional parameter in addition to the acoustic features. The language input provides a measure of classification of the input speech utterance. A combined classifier is used to make a linear decision on the emotion of the uttered speech as an arousal or valence. When the decision is fed to the Generative Factor Analyzed Hidden Markov Model (GFA-HMM) it evidently substantiates to be a better method with good accuracy rate of classification of whether the speaker is entangled in the conversation or vice-versa. The proposed method produced highly satisfactory results for the Linguistic Data Consortium (LDC) emotional prosody dataset.

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