http://www.ffri.hr/~ibrdar/komunikacija/seminari/Ververidis,%202006%20-%20Emotional%20speech%20recognition.pdf

Emotional speech recognition: Resources, features, and methods

Emotion recognition from speech has emerged as an important research area in the recent past. In this regard, review of existing work on emotional speech processing is useful for carrying out further research. In this paper, the recent literature on speech emotion recognition has been presented considering the issues related to emotional speech corpora, different types of speech features and models used for recognition of emotions from speech. Thirty two representative speech databases are reviewed in this work from point of view of their language, number of speakers, number of emotions, and purpose of collection. The issues related to emotional speech databases used in emotional speech recognition are also briefly discussed. Literature on different features used in the task of emotion recognition from speech is presented. The importance of choosing different classification models has been discussed along with the review. The important issues to be considered for further emotion recognition research in general and in specific to the Indian context have been highlighted where ever necessary.

/pdf/emotion-recognition-from-speech-a-review-2bhwxdu8ml.pdf

Emotion recognition from speech: a review

Introduction BASIC RESEARCH METHODS AND PROCEDURES 2. Measuring facial actions 3. Vocal expression of affect 4. Proxemics, kinesics and gaze 5. Conducting judgment studies: some methodological issues RESEARCH APPLICATIONS IN NONVERBAL BEHAVIOR 6. Nonverbal behavior and interpersonal sensitivity 7. Nonverbal behavior in education 8. Nonverbal behavior and psychopathology 9. Research methods in detecting deception research 10. Nonverbal communication coding systems of committed couples 11. Macrovariables in affective expression in women with breast cancer participating in support groups SUPPLEMENTARY MATERIALS 12. Technical issues in recording nonverbal behavior Appendix: Methodological issues in studying nonverbal behavior

/pdf/the-new-handbook-of-methods-in-nonverbal-behavior-research-5asqu0semk.pdf

The New Handbook of Methods in Nonverbal Behavior Research

Speech is an effective medium to express emotions and attitude through language. Finding the emotional content from a speech signal and identify the emotions from the speech utterances is an important task for the researchers. Speech emotion recognition has considered as an important research area over the last decade. Many researchers have been attracted due to the automated analysis of human affective behaviour. Therefore a number of systems, algorithms, and classifiers have been developed and outlined for the identification of emotional content of a speech from a person's speech. In this study, available literature on various databases, different features and classifiers have been taken in to consideration for speech emotion recognition from assorted languages.

Databases, features and classifiers for speech emotion recognition: a review

In this paper, global and local prosodic features extracted from sentence, word and syllables are proposed for speech emotion or affect recognition. In this work, duration, pitch, and energy values are used to represent the prosodic information, for recognizing the emotions from speech. Global prosodic features represent the gross statistics such as mean, minimum, maximum, standard deviation, and slope of the prosodic contours. Local prosodic features represent the temporal dynamics in the prosody. In this work, global and local prosodic features are analyzed separately and in combination at different levels for the recognition of emotions. In this study, we have also explored the words and syllables at different positions (initial, middle, and final) separately, to analyze their contribution towards the recognition of emotions. In this paper, all the studies are carried out using simulated Telugu emotion speech corpus (IITKGP-SESC). These results are compared with the results of internationally known Berlin emotion speech corpus (Emo-DB). Support vector machines are used to develop the emotion recognition models. The results indicate that, the recognition performance using local prosodic features is better compared to the performance of global prosodic features. Words in the final position of the sentences, syllables in the final position of the words exhibit more emotion discriminative information compared to the words and syllables present in the other positions.

Emotion recognition from speech using global and local prosodic features

ATR's CHATR is a corpus-based text-to-speech (TTS) synthesis system that selects concatenation units from a natural speech database. The system's approach enables us to create a voice output communication aid (VOCA) using the voices of individuals who are anticipating the loss of phonatory functions. The advantage of CHATR is that individuals can use their own voice for communication even after vocal loss. This paper reports on a case study of the development of a VOCA using recordings of Japanese read speech (i.e., oral reading) from an individual with amyotrophic lateral sclerosis (ALS). In addition to using the individual's speech, we designed a speech database that could reproduce the characteristics of natural utterances in both general and specific situations. We created three speech corpora in Japanese to synthesize ordinary daily speech (i.e., in a normal speaking style): (1) a phonetically balanced sentence set, to assure that the system was able to synthesize all speech sounds; (2) readings of manuscripts, written by the same individual, for synthesizing talks regularly given as a source of natural intonation, articulation and voice quality; and (3) words and short phrases, to provide daily vocabulary entries for reproducing natural utterances in predictable situations. By combining one or more corpora, we were able to create four kinds of source database for CHATR synthesis. Using each source database, we synthesized speech from six test sentences. We selected the source database to use by observing selected units of synthesized speech and by performing perceptual experiments where we presented the speech to 20 Japanese native speakers. Analyzing the results of both observations and evaluations, we selected a source database compiled from all corpora. Incorporating CHATR, the selected source database, and an input acceleration function, we developed a VOCA for the individual to use in his daily life. We also created emotional speech source databases designed for loading separately to the VOCA in addition to the compiled speech database.

Speech Database Design for a Concatenative Text-to-Speech Synthesis System for Individuals with Communication Disorders

The paper describes an application concept of an affective communication system for people with disabilities and elderly people, summarizes the universal nature of emotion and its vocal expression, and reports on the work on designing a corpus database of emotional speech for a speech synthesis in the proposed system. Three corpora of emotional speech (joy, anger and sadness) have been designed and tested for the use with CHATR, the concatenated speech synthesis system at ATR. Each text corpus was designed to bring out a speaker's emotion. The result of perceptual experiments was proved to be significant and so was the result of CHATR synthesized speech. This indicates that the subjects successfully identified the emotion types of the synthesized speech from implicit phonetic information and hence this study has proved the validity of using a corpus of emotional speech as a database for the concatenated speech synthesis system.

Emotional speech as an effective interface for people with special needs

Study of Emotion in Speech: Findings from Perceptual Experiments

https://people.cs.pitt.edu/~litman/courses/ads/readings/iida03.pdf

A corpus-based speech synthesis system with emotion

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Speech Database Design for a Concatenative Text-to-Speech Synthesis System for Individuals with Communication Disorders

Emotional speech as an effective interface for people with special needs

Study of Emotion in Speech: Findings from Perceptual Experiments

A corpus-based speech synthesis system with emotion