Cultural differences have been one of the many factors that can cause failures in speech emotion analysis. If this cultural parameter could be regarded as noise artifacts in detecting emotion in speech, we could then extract pure emotion speech signal from the raw emotional speech. In this paper we use the amplitude spectral subtraction (ASS) method to profile the emotion from raw emotional spe...

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ens...

An important part of the human-computer interaction process is speech emotion recognition (SER), which has been receiving more attention in recent years. However, although a wide diversity methods proposed SER, these approaches still cannot improve performance. A key issue low performance SER system how to effectively extract emotion-oriented features. In this paper, we propose novel algorithm,...

The generation of expressive speech is a great challenge for text-to-speech synthesis in audiobooks. One of the most important factors is the variation in speech emotion or voice style. In this work, we developed a method to predict the emotion from a sentence so that we can convey it through the synthetic voice. It consists of combining a standard emotion-lexicon based technique with the polar...

Emotion recognition, a key step of affective computing, is the process of decoding an embedded emotional message from human communication signals, e.g. visual, audio, and/or other physiological cues. It is well-known that speech is the main channel for human communication and thus vital in the signalling of emotion and semantic cues for the correct interpretation of contexts. In the verbal chan...

In control vector-based expressive speech synthesis, the emotion/style control vector defined in the categorical (CAT) emotion space is uneasy to be precisely defined by the user to synthesize the speech with the desired emotion/style. This paper applies the arousal-valence (AV) space to the multiple regression hidden semi-Markov model (MRHSMM)-based synthesis framework for expressive speech sy...

Synthesizing emotional speech by means of conversion from neutral speech allows us to generate emotional speech from many existing Text-to-Speech (TTS) systems. How much of the target emotion can be portrayed by the generated speech is largely dependent on the emotion data used to train the mapping function for voice transformation. In this paper, we introduce a method to pre-process the emotio...

One of the largest challenges in speaker recognition is dealing with speaker-emotion variability problem. Nowadays, compensation techniques are the main solutions to this problem. In these methods, all kinds of speakers’ emotion speech should be elicited thus it is not user-friendly in the application. Therefore the basic problem is how to get the distribution of speakers’ emotion speech and ho...

This paper describes and evaluates four different HSMM (hidden semi-Markov model) training methods for HMM-based synthesis of emotional speech. The first method, called emotion-dependent modelling, uses individual models trained for each emotion separately. In the second method, emotion adaptation modelling, at first a model is trained using neutral speech, and thereafter adaptation is performe...