Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Primitives-based evaluation and estimation of emotions in speech
Speech Communication
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in Human-Computer Interaction - Special issue on emotion-aware natural interaction
IEEE Transactions on Affective Computing
Speech emotion recognition system based on L1 regularized linear regression and decision fusion
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Investigating the use of formant based features for detection of affective dimensions in speech
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
AVEC 2011-the first international audio/visual emotion challenge
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
F0 contour of prosodic word in happy speech of mandarin
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection
IEEE Transactions on Audio, Speech, and Language Processing
Emotion recognition from speech: a review
International Journal of Speech Technology
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This paper proposes the use of neutral reference models to detect local emotional prominence in the fundamental frequency. A novel approach based on functional data analysis (FDA) is presented, which aims to capture the intrinsic variability of F0 contours. The neutral models are represented by a basis of functions and the testing F0 contour is characterized by the projections onto that basis. For a given F0 contour, we estimate the functional principal component analysis (PCA) projections, which are used as features for emotion detection. The approach is evaluated with lexicon-dependent (i.e., one functional PCA basis per sentence) and lexicon-independent (i.e., a single functional PCA basis across sentences) models. The experimental results show that the proposed system can lead to accuracies as high as 75.8% in binary emotion classification, which is 6.2% higher than the accuracy achieved by a benchmark system trained with global F0 statistics. The approach can be implemented at sub-sentence level (e.g., 0.5s segments), facilitating the detection of localized emotional information conveyed within the sentence. The approach is validated with the SEMAINE database, which is a spontaneous corpus. The results indicate that the proposed scheme can be effectively employed in real applications to detect emotional speech.