Journal ArticleDOI
Context-Independent Multilingual Emotion Recognition from Speech Signals
Vladimir Hozjan,Zdravko Kacic +1 more
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TLDR
Among speaker-dependent, monolingual, and multilingual emotion recognition, the difference between emotion Recognition with all high-level features and emotion recognition with database-specific emotional features is smallest for mult bilingual emotion recognition—3.84%.Abstract:
This paper presents and discusses an analysis of multilingual emotion recognition from speech with database-specific emotional features. Recognition was performed on English, Slovenian, Spanish, and French InterFace emotional speech databases. The InterFace databases included several neutral speaking styles and six emotions: disgust, surprise, joy, fear, anger and sadness. Speech features for emotion recognition were determined in two steps. In the first step, low-level features were defined and in the second high-level features were calculated from low-level features. Low-level features are composed from pitch, derivative of pitch, energy, derivative of energy, and duration of speech segments. High-level features are statistical presentations of low-level features. Database-specific emotional features were selected from high-level features that contain the most information about emotions in speech. Speaker-dependent and monolingual emotion recognisers were defined, as well as multilingual recognisers. Emotion recognition was performed using artificial neural networks. The achieved recognition accuracy was highest for speaker-dependent emotion recognition, smaller for monolingual emotion recognition and smallest for multilingual recognition. The database-specific emotional features are most convenient for use in multilingual emotion recognition. Among speaker-dependent, monolingual, and multilingual emotion recognition, the difference between emotion recognition with all high-level features and emotion recognition with database-specific emotional features is smallest for multilingual emotion recognition—3.84%.read more
Citations
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Journal ArticleDOI
Survey on speech emotion recognition: Features, classification schemes, and databases
TL;DR: A survey of speech emotion classification addressing three important aspects of the design of a speech emotion recognition system, the choice of suitable features for speech representation, and the proper preparation of an emotional speech database for evaluating system performance are addressed.
Journal ArticleDOI
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011
TL;DR: Important topics from different classification techniques, such as databases available for experimentation, appropriate feature extraction and selection methods, classifiers and performance issues are discussed, with emphasis on research published in the last decade.
Journal ArticleDOI
Anger recognition in speech using acoustic and linguistic cues
TL;DR: The present study elaborates on the exploitation of both linguistic and acoustic feature modeling for anger classification by evaluating classification success using the f1 measurement in addition to overall accuracy figures.
BookDOI
Affective Information Processing
Jianhua Tao,Tieniu Tan +1 more
TL;DR: This ground-breaking book provides a comprehensive overview and significant insight into the field of affective information processing, and will prove a valuable reference tool and resource.
References
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The Emotional Brain
TL;DR: In The Emotional Brain, Joseph LeDoux investigates the origins of human emotions and explains that many exist as part of complex neural systems that evolved to enable us to survive.
The HTK book
TL;DR: The Fundamentals of HTK: General Principles of HMMs, Recognition and Viterbi Decoding, and Continuous Speech Recognition.
Journal ArticleDOI
Acoustic profiles in vocal emotion expression.
Rainer Banse,Klaus R. Scherer +1 more
TL;DR: Findings on decoding replicate earlier findings on the ability of judges to infer vocally expressed emotions with much-better-than-chance accuracy, including consistently found differences in the recognizability of different emotions.