Emotion Recognition from Speech using Discriminative Features
نویسندگان
چکیده
منابع مشابه
Emotion Recognition from Speech using Discriminative Features
Creating an accurate Speech Emotion Recognition (SER) system depends on extracting features relevant to that of emotions from speech. In this paper, the features that are extracted from the speech samples include Mel Frequency Cepstral Coefficients (MFCC), energy, pitch, spectral flux, spectral roll-off and spectral stationarity. In order to avoid the 'curse of dimensionality', statis...
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Emotion recognition, a key step of affective computing, is the process of decoding an embedded emotional message from human communication signals, e.g. visual, audio, and/or other physiological cues. It is well-known that speech is the main channel for human communication and thus vital in the signalling of emotion and semantic cues for the correct interpretation of contexts. In the verbal chan...
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Recent developments in man–machine interaction have motivated researchers to recognize human emotion from speech signals. In this study, we propose using nonlinear dynamics features (NLDs) for emotion recognition. NLDs are extracted from the geometrical properties of the reconstructed phase space of speech signals. The traditional prosodic and spectral features are also used as a benchmark. The...
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In early research the basic acoustic features were the primary choices for emotion recognition from speech. Most of the feature vectors were composed with the simple extracted pitch-related, intensity related, and duration related attributes, such as maximum, minimum, median, range and variability values. However, researchers are still debating what features influence the recognition of emotion...
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Emotion recognition from speech has emerged as an important research area in the recent past. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into seven emotional states including anger, boredom, disgust, fear, happiness, sadness and neutral. The speech samples are from Berlin emotional database and the features extracted from these uttera...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2014
ISSN: 0975-8887
DOI: 10.5120/17775-8913