A significant part of the work required to create a high quality speech synthesizer is the creation of “synthetic voices”. Reusing an existing voice database and making it sound like a different speaker, or like the same speaker in a different emotional state, or using a different speaking style, is obviously important for increasing the efficiency in creating voice options for a synthesizer. T...

In this paper, we propose a method for automatically detecting deceptive speech by relying on predicted scores derived from emotion dimensions such as arousal, valence, regulation, and emotion categories. The scores are derived from task-dependent models trained on the GEMEP emotional speech database. Inputs from the INTERSPEECH 2016 Computational Paralinguistics Deception sub-challenge are pro...

Speech emotion recognition is mostly considered in clean speech. In this paper, joint spectro-temporal features (RS features) are extracted from an auditory model and are applied to detect the emotion status of noisy speech. The noisy speech is derived from the Berlin Emotional Speech database with added white and babble noises under various SNR levels. The clean train/noisy test scenario is in...

An emotion is made of several components such as physiological changes in the body, subjective feelings, and expressive behaviours. These changes in speech signal are mainly observed in prosody parameters such as pitch, duration and energy. In this work, prosody parameters are modified using instants of significant excitation (epochs) and these instants are detected using Zero Frequency Filteri...

The goal of this MSc project was to build unit selection voice that could portray emotions in various intensities. A suitable definition of emotion was developed along with a descriptive framework that supported the work carried out. Two speakers were recorded portraying happy and angry speaking styles, additionally a neutral database was also recorded. One voice was built for each speaker that...

Speaker Recognition is a major task when security applications through speech input are needed. Regarding speaker identity, several factors of variability must be considered: a) Factors concerning peculiar intra-speaker variability (manner of speaking, inter-session variability, dialectal variations, emotional condition, etc.) or forced intra-speaker variability (Lombard effect, cocktail-party ...

Speech Emotion Recognition (SER) plays a predominant role in human-machine interaction. SER is challenging task because of number complexities involved it. For an accurate emotion classification system, feature extraction the first and important step carried out on speech signals. And after features are extracted, it very to select best all reject redundant least features. Feature selection met...

In this paper we present text dependent speaker recognition with an enhancement of detecting the emotion of the speaker prior using the hybrid FFBN and GMM methods. The emotional state of the speaker influences recognition system. Mel-frequency Cepstral Coefficient (MFCC) feature set is used for experimentation. To recognize the emotional state of a speaker Gaussian Mixture Model (GMM) is used ...

In the study of expressive speech communication, it is commonly accepted that the emotion perceived by the listener is a good approximation of the intended emotion conveyed by the speaker. This paper analyzes the validity of this assumption by comparing the mismatches between the assessments made by naı̈ve listeners and by the speakers that generated the data. The analysis is based on the hypoth...

Due to the advance of speech technologies and their increasing usage in various applications, automatic recognition of emotions in speech represents one of the emerging fields in human-computer interaction. This paper deals with several topics related to automatic emotional speech recognition, most notably with the improvement of recognition accuracy by lowering the dimensionality of the featur...