Relations of sex and dialect to reduction
Speech Communication
Machine Learning - Special issue on inductive transfer
Automatic Classification of Expressiveness in Speech: A Multi-corpus Study
Speaker Classification II
Language---Independent Speaker Classification over a Far---Field Microphone
Speaker Classification II
What Should a Generic Emotion Markup Language Be Able to Represent?
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
An Extension of MPML with Emotion Recognition Functions Attached
IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
Mind as Machine: A History of Cognitive Science
Mind as Machine: A History of Cognitive Science
PEAKS - A system for the automatic evaluation of voice and speech disorders
Speech Communication
Social signal processing: Survey of an emerging domain
Image and Vision Computing
Image and Vision Computing
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
On the use of nonverbal speech sounds in human communication
COST 2102'07 Proceedings of the 2007 COST action 2102 international conference on Verbal and nonverbal communication behaviours
Class-level spectral features for emotion recognition
Speech Communication
Semi-supervised speaker identification under covariate shift
Signal Processing
Computer Speech and Language
Opensmile: the munich versatile and fast open-source audio feature extractor
Proceedings of the international conference on Multimedia
RANSAC-based training data selection for emotion recognition from spontaneous speech
Proceedings of the 3rd international workshop on Affective interaction in natural environments
The voice of personality: mapping nonverbal vocal behavior into trait attributions
Proceedings of the 2nd international workshop on Social signal processing
Automatically Assessing Personality from Speech
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies
IEEE Transactions on Affective Computing
The Cross-Modal and Cross-Cultural Processing of Affective Information
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Automatic speech emotion recognition using modulation spectral features
Speech Communication
Affective speaker state analysis in the presence of reverberation
International Journal of Speech Technology
Time-Frequency features extraction for infant directed speech discrimination
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
Multiple classifier systems for the recogonition of human emotions
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Paralinguistics in speech and language-State-of-the-art and the challenge
Computer Speech and Language
Vocal markers of emotion: Comparing induction and acting elicitation
Computer Speech and Language
Words that Fascinate the Listener: Predicting Affective Ratings of On-Line Lectures
International Journal of Distance Education Technologies
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The field of computational paralinguistics is currently emerging from loosely connected research on speaker states, traits, and vocal behaviour. Starting from a broad perspective on the state-of-the-art in this field, we combine these facts with a bit of 'tea leaf reading' to identify ten currently dominant trends that might also characterise the next decade of research: taking into account more tasks and task interdependencies, modelling paralinguistic information in the continuous domain, agglomerating and evaluating on large amounts of heterogeneous data, exploiting more and more types of features, fusing linguistic and non-linguistic phenomena, devoting more effort to optimisation of the machine learning aspects, standardising the whole processing chain, addressing robustness and security of systems, proceeding to evaluation in real-life conditions, and finally overcoming cross-language and cross-cultural barriers. We expect that following these trends we will see an increase in the 'social competence' of tomorrow's speech and language processing systems.