Catálogo de publicaciones - libros

The affections of a person can be expressed by non-verbal methods such as facial expressions, gestures, postures and expressions from eyes. While implicit language like emotional metaphor is also an important way to express one’s affections. Different kinds of emotional metaphors in Chinese and their characteristics are proposed, including happiness, sadness, anger, fear and surprise. Experiment result shows the characteristics of Chinese emotional metaphors are reasonable. The importance of emotional metaphors in emotion recognition is also discussed.

Both of the prosody and spectral features are important for emotional speech synthesis. Besides prosody effects, voice quality and articulation parameters are the factors that should be considered to modify in emotional speech synthetic systems. Generally, rules and filters are designed to process these parameters respectively. This paper proves that by modifying spectral envelope, the voice quality and articulation could be adjusted as a whole. Thus, it will not need to modify each of the parameter separately depending on rules. Accordingly, it will make the synthetic system more flexible by designing an automatic spectral envelope model based on some machine learning methods. The perception test in this paper also shows that when prosody and spectral features are all modified, the best emotional synthetic speech will be obtained.

Recent researches in speech synthesis are mainly focused on naturalness, and the emotional speech synthesis becomes one of the highlighted research topics. Although quite a many studies on emotional speech in English or Japanese have been addressed, the studies in Korean can seldom be found. This paper presents an analysis of emotional speech in Korean. Emotional speech features related to human speech prosody, such as F0, the duration, and the amplitude with their variations, are exploited. Their attribution to three different types of typical human speech is tried to be quantified and modeled. By utilizing the analysis results, emotional voice conversion from the neutral speech to the emotional one is also performed and tested.

In this paper, a semantic lexicon in the field of feelings and emotions is presented. This lexicon is described with an ontology. Then, we describe a system to annotate emotions in a text and, finally, we show how these annotations allow a textual navigation.

This paper presents an approach to emotion recognition from speech signals. In this approach, the intonation groups (IGs) of the input speech signals are firstly extracted. The speech features in each selected intonation group are then extracted. With the assumption of linear mapping between feature spaces in different emotional states, a feature compensation approach is proposed to characterize the feature space with better discriminability among emotional states. The compensation vector with respect to each emotional state is estimated using the Minimum Classification Error (MCE) algorithm. The IG-based feature vectors compensated by the compensation vectors are used to train the Gaussian Mixture Models (GMMs) for each emotional state. The emotional state with the GMM having the maximal likelihood ratio is determined as the final output. The experimental result shows that IG-based feature extraction and compensation can obtain encouraging performance for emotion recognition.

This paper presents a spectral transformation method for emotional speech synthesis based on voice conversion framework. Three emotions are studied, including anger, happiness and sadness. For the sake of high naturalness, superior speech quality and emotion expressiveness, our original STASC system is modified by introducing a new feature selection strategy and hierarchical codebook mapping procedure. Our result shows that the LSF coefficients at low frequency carry more emotion-relative information, and therefore only these coefficients are converted. Listening tests prove that the proposed method can achieve a satisfactory balance between emotional expression and speech quality of converted speech signals.

Mandarin is a typical tone language in which a syllable possesses several tone types. While these tone types have rather clear manifestations in the fundamental frequency contour ( contour) in isolated syllables, they vary considerably in affective speech due to the influences of the speaker’s mood. In the paper the Fujisaki model based on the measured contour is modified to adapt for affective Mandarin, and a novel approach is proposed to extract the parameters of the model automatically without any manual labels information such as boundary labels, tone types and syllable timing, etc. The preliminary statistic result shows the model is feasible for the affective speech study.

This paper presents a conception of emotion space modeling using psychological research for reference. Based on this conception, this paper studies the distribution of the seven emotions in spoken Chinese, including joy, anger, surprise, fear, disgust, sadness and neutral, in the two dimensional space of valence and arousal, and analyses the relationship between the dimensional ratings and the prosodic characteristics in terms of F0 maximum, minimum, range and mean. The findings show that the conception of emotion modeling is helpful to describe and distinguish emotions.

The performance of speaker recognition system is easily disturbed by the changes of the internal states of human. The ongoing work proposes an approach of speech emotion-state conversion to improve the performance of speaker identification system over various affective speech. The features of neutral speech are modified according to statistical prosodic parameters of emotion utterances. Speaker models are generated based on the converted speech. The experiments conducted on an emotion corpus with 14 emotion states shows promising results with an improved performance by 7.2%.

Global integration and migration force people to learn additional languages. With respect to major languages, the acquisition is already initiated at primary school but according to their missing daily practice, many speakers keep a strong accent for longterm which may cause integration problems in new social or working environments. The possibility of later pronunciation improvements is limited since an experienced teacher and single education are required. Computer-assisted teaching methods have been established during the last decade.

Common methods do either not include a distinct user feedback (vocabulary trainer playing a reference pattern) or widely rely on fully automatic methods (speech recognition regarding the target language) causing evaluation mistakes, in particular, across the border of language groups.

The authors compiled an audiovisual database and set up an automatic system for the accent reduction (called ) by using recordings of 11 native Russian speakers learning German and 10 native German reference speakers. The system feedback is given within a multi modal scenario.