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Emotion Recognition and Conversion for Mandarin Speech
2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009In this study, some research activities on expressive speech recognition and conversion will be introduced. A database consisting of five kinds of speech emotions (i.e. happiness, sadness, surprise, anger and neutral) is used. Not only those traditional features such as mfcc, plp, and pitch are studied, but also a new feature extraction method based on
Yu Zhou +3 more
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Emotion Prompting for Speech Emotion Recognition
INTERSPEECH 2023, 2023Xingfa Zhou +5 more
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Transfer Learning for Speech Emotion Recognition
2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2019Speech emotion recognition is still challenging due to the complexity of emotion. Most of the current research is based on the enough data, and the training data and test data are from the same corpus. Obviously, this is not in line with the actual application scene. In addition, the emotion annotation of large amounts speech data is also a challenging
Zhijie Han 0001 +2 more
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Evaluation of emotion recognition from speech
2012 20th Signal Processing and Communications Applications Conference (SIU), 2012Over the last few years, interest on paralinguistic information classification has grown considerably. However, in comparison to related speech processing tasks such as Automatic Speech and Speaker Recognition, practically no standardised corpora and test-conditions exist to compare performances under exactly the same conditions.
Elif Bozkurt, Engin Erzin
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NMF features for speech emotion recognition
Proceedings of the 2009 International Conference on Hybrid Information Technology, 2009There are numerous algorithms to detect emotion from speech signals. Among the algorithms, we selected spectral analysis and fused Non-negative Matrix Factorization (NMF) for obvios emotion classification. The algorithm has been tested in several different ways by varying NMF and the speech database. The experimental results show performance of 90% and
Kyungjoong Jeong +2 more
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2015
Recent years have been marked by a growing need for systems that can grasp human emotions and in particular, recognize emotions. Emotions lie at the centre of any social communication and form the basis for an intelligent and meaningful interaction. The chapter further discusses the acoustic correlates of emotions and describes various techniques and ...
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Recent years have been marked by a growing need for systems that can grasp human emotions and in particular, recognize emotions. Emotions lie at the centre of any social communication and form the basis for an intelligent and meaningful interaction. The chapter further discusses the acoustic correlates of emotions and describes various techniques and ...
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Speech Emotion Recognition and Intensity Estimation
2004In this paper, a system for speech emotion analysis is presented. On a corpus of over 1700 utterances from an individual, the feature vector stream is extracted for each utterance based on short time log frequency power coefficients (LFCC). Using the feature vector streams, we trained Hidden Markov Models (HMMs) to recognize seven basic categories ...
Mingli Song +3 more
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Emotion recognition by speech signals
8th European Conference on Speech Communication and Technology (Eurospeech 2003), 2003Oh-Wook Kwon +3 more
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Robust Recognition of Emotion from Speech
2006This paper presents robust recognition of a subset of emotions by animated agents from salient spoken words. To develop and evaluate the model for each emotion from the chosen subset, both the prosodic and acoustic features were used to extract the intonational patterns and correlates of emotion from speech samples. The computed features were projected
Mohammed E. Hoque 0001 +2 more
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