An effective method based on GMM is proposed in this paper for speech emotional recognition; a compensation transformation is introduced in the recognition stage to reduce the influence of variations in speech characteristics and noise. The extraction of emotional features includes the globe feature, time series structure feature, LPCC, MFCC and PLP. Five human emotions (happiness, angry, surpr...

An emotional speech corpus of Finnish was collected that includes utterances of four emotional states of speakers. More than 40 prosodic features were derived and automatically computed for the speech samples. Statistical classification experiments with kNN classifier and human listening tests indicate that emotion recognition performance comparable to human listeners can be achieved.

An emotional speech corpus of Finnish was collected that includes utterances of four emotional states of speakers. More than 40 prosodic features were derived and automatically computed for the speech samples. Statistical classification experiments with kNN classifier and human listening tests indicate that emotion recognition performance comparable to human listeners can be achieved.

An emotional speech corpus of Finnish was collected that includes utterances of four emotional states of speakers. More than 40 prosodic features were computed for speech samples. Statistical classification experiments indicate that emotion recognition performance comparable to human listeners can be achieved. However, due to the small amount of data, the results must be interpreted cautiously.

The interest in emotion recognition from speech has increased in the last decade. Emotion recognition can improve the quality of services and the quality of life of people. One of the main problems in emotion recognition from speech is to find suitable features to represent the phenomenon. This paper proposes new features based on the energy content of wavelet based time-frequency (TF) represen...

The automatic recognition of emotion from speech is a mature research field with a large number of publicly available corpora. However, to the best of the authors knowledge, none of these datasets consist solely of emotional speech samples from individuals with mental, neurological and/or physical disabilities. Yet, such individuals could benefit from speech-based assistive technologies to enha...

The use of speech in human-machine interaction is increasing as the computer interfaces are becoming more complex but also more useable. These interfaces make use of the information obtained from the user through the analysis of different modalities and show a specific answer by means of different media. The origin of the multimodal systems can be found in its precursor, the “Put-That-There” sy...

This present paper highlights a methodology for Emotion Recognition based on Skew Symmetric Gaussian Mixture Model classifier and MFCC-SDC ceptral coefficients as the features for the recognition of various emotions from the generated data-set of emotional voices belonging to students of both genders in GITAM University. For training and testing of the developed methodology, the data collection...

Speech emotional database and recognition is the challenging part of human computer interaction. The current research focuses towards the detection of emotion in various situations, while the database demands more to fetch out the work of recognition. The study investigates the various existing speech databases containing various basic emotions, enhancing the appropriate database development as...

This paper presents a Mandarin audio-visual recognition system dealing with noisy and emotional speech signal. In the proposed approach, we extract the visual features of the lips. These features are very important to the recognition system especially in noisy condition or with emotional effects. In this recognition system, we propose to use the weighted-discrete KNN as the classifier and compa...