Employing Emotion Cues to Verify Speakers in Emotional Talking Environments

Usually, people talk neutrally in environments where there are no abnormal talking conditions such as stress and emotion. Other emotional conditions that might affect people talking tone like happiness, anger, and sadness. Such emotions are directly affected by the patient health status. In neutral talking environments, speakers can be easily verified, however, in emotional talking environments, speakers cannot be easily verified as in neutral talking ones. Consequently, speaker verification systems do not perform well in emotional talking environments as they do in neutral talking environments. In this work, a two-stage approach has been employed and evaluated to improve speaker verification performance in emotional talking environments. This approach employs speaker emotion cues (text-independent and emotion-dependent speaker verification problem) based on both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. The approach is comprised of two cascaded stages that combines and integrates emotion recognizer and speaker recognizer into one recognizer. The architecture has been tested on two different and separate emotional speech databases: our collected database and Emotional Prosody Speech and Transcripts database. The results of this work show that the proposed approach gives promising results with a significant improvement over previous studies and other approaches such as emotion-independent speaker verification approach and emotion-dependent speaker verification approach based completely on HMMs.Comment: Journal of Intelligent Systems, Special Issue on Intelligent Healthcare Systems, De Gruyter, 201

Nonparallel Emotional Speech Conversion

We propose a nonparallel data-driven emotional speech conversion method. It enables the transfer of emotion-related characteristics of a speech signal while preserving the speaker's identity and linguistic content. Most existing approaches require parallel data and time alignment, which is not available in most real applications. We achieve nonparallel training based on an unsupervised style transfer technique, which learns a translation model between two distributions instead of a deterministic one-to-one mapping between paired examples. The conversion model consists of an encoder and a decoder for each emotion domain. We assume that the speech signal can be decomposed into an emotion-invariant content code and an emotion-related style code in latent space. Emotion conversion is performed by extracting and recombining the content code of the source speech and the style code of the target emotion. We tested our method on a nonparallel corpora with four emotions. Both subjective and objective evaluations show the effectiveness of our approach.Comment: Published in INTERSPEECH 2019, 5 pages, 6 figures. Simulation available at http://www.jian-gao.org/emoga

Anchor model fusion for emotion recognition in speech

Proceedings of Joint COST 2101 and 2102 International Conference, BioID_MultiComm 2009, Madrid (Spain)The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-04391-8_7In this work, a novel method for system fusion in emotion recognition for speech is presented. The proposed approach, namely Anchor Model Fusion (AMF), exploits the characteristic behaviour of the scores of a speech utterance among different emotion models, by a mapping to a back-end anchor-model feature space followed by a SVM classifier. Experiments are presented in three different databases: Ahumada III, with speech obtained from real forensic cases; and SUSAS Actual and SUSAS Simulated. Results comparing AMF with a simple sum-fusion scheme after normalization show a significant performance improvement of the proposed technique for two of the three experimental set-ups, without degrading performance in the third one.This work has been financed under project TEC2006-13170-C02-01

Proposing a hybrid approach for emotion classification using audio and video data

Emotion recognition has been a research topic in the field of Human-Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%

Speaker-independent negative emotion recognition

This work aims to provide a method able to distinguish between negative and non-negative emotions in vocal interaction. A large pool of 1418 features is extracted for that purpose. Several of those features are tested in emotion recognition for the first time. Next, feature selection is applied separately to male and female utterances. In particular, a bidirectional Best First search with backtracking is applied. The first contribution is the demonstration that a significant number of features, first tested here, are retained after feature selection. The selected features are then fed as input to support vector machines with various kernel functions as well as to the K nearest neighbors classifier. The second contribution is in the speaker-independent experiments conducted in order to cope with the limited number of speakers present in the commonly used emotion speech corpora. Speaker-independent systems are known to be more robust and present a better generalization ability than the speaker-dependent ones. Experimental results are reported for the Berlin emotional speech database. The best performing classifier is found to be the support vector machine with the Gaussian radial basis function kernel. Correctly classified utterances are 86.73%±3.95% for male subjects and 91.73%±4.18% for female subjects. The last contribution is in the statistical analysis of the performance of the support vector machine classifier against the K nearest neighbors classifier as well as the statistical analysis of the various support vector machine kernels impact. © 2010 IEEE