Feature Extraction from Speech Data for Emotion Recognition
نویسندگان
چکیده
منابع مشابه
Feature Extraction and Selection in Speech Emotion Recognition
Speech Emotion Recognition (SER) is a hot research topic in the field of Human Computer Interaction (HCI). In this paper, we recognize three emotional states: happy, sad and neutral. The explored features include: energy, pitch, linear predictive spectrum coding (LPCC), Mel-frequency spectrum coefficients (MFCC), and Mel-energy spectrum dynamic coefficients (MEDC). A German Corpus (Berlin Datab...
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Speech Emotion Recognition (SER) has achieved some substantial progress in the past few decades since the dawn of emotion and speech research. In many aspects, various research efforts have been made in an attempt to achieve human-like emotion recognition performance in real-life settings. However, with the availability of speech data obtained from different devices and varied acquisition condi...
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Emotional expression and understanding are normal instincts of human beings, but automatical emotion recognition from speech without referring any language or linguistic information remains an unclosed problem. The limited size of existing emotional data samples, and the relative higher dimensionality have outstripped many dimensionality reduction and feature selection algorithms. This paper fo...
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This paper presents an approach to emotion recognition from speech signals. In this approach, the intonation groups (IGs) of the input speech signals are firstly extracted. The speech features in each selected intonation group are then extracted. With the assumption of linear mapping between feature spaces in different emotional states, a feature compensation approach is proposed to characteriz...
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ژورنال
عنوان ژورنال: Journal of Advances in Computer Networks
سال: 2014
ISSN: 1793-8244
DOI: 10.7763/jacn.2014.v2.76