Open Access
Analysis and modelling of emotional speech in spanish
TLDR
In this article, the authors present the prosodic analysis, modelling and evaluation of the Spanish Emotional Speech Database including four emotions: happiness, sadness, cold anger, and surprise.Abstract:
The importance of speech prosody for conveying emotional information has been extensively underlined in the literature. Major elements such as pitch, tempo and stress are presented as the main acoustic correlates of emotion in human speech. Nevertheless, as several authors have shown, voice quality is also a relevant feature in emotion recognition. In this paper, we present the prosodic analysis, modelling and evaluation of the Spanish Emotional Speech Database including four emotions: happiness, sadness, cold anger and surprise. Our results show that, for Spanish, the contribution of prosody to the recognisability of the uttered emotion greatly varies from one to another, with sadness and surprise being more supra segmental, and happiness and cold anger being rather segmental.read more
Citations
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Journal ArticleDOI
Emotional speech recognition: Resources, features, and methods
TL;DR: This paper overviews emotional speech recognition having in mind three goals to provide an up-to-date record of the available emotional speech data collections, and examines separately classification techniques that exploit timing information from which that ignore it.
Journal ArticleDOI
Emotion recognition from speech: a review
TL;DR: 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.
Proceedings Article
Emotional speech synthesis: a review.
TL;DR: An overview of what has been done in the field of emotion effects to synthesised speech is given, pointing out the inherent properties of the various synthesis techniques used, summarising the prosody rules employed, and taking a look at the evaluation paradigms.
Journal ArticleDOI
Databases, features and classifiers for speech emotion recognition: a review
TL;DR: 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.
A State of the Art Review on Emotional Speech Databases
TL;DR: This study concludes that automated emotion recognition on these databases cannot achieve a correct classification that exceeds 50% for the four basic emotions, i.e., twice as much as random selection.
References
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Journal ArticleDOI
Implementation and testing of a system for producing emotion-by-rule in synthetic speech
Iain R. Murray,John L. Arnott +1 more
TL;DR: A system is described which adds simulated emotion effects to synthetic speech, developed for use in voice prosthesis systems for non-vocal disabled persons, although it could be used to enhance any application which uses rule-based synthetic speech.
Proceedings Article
Emotional speech synthesis: from speech database to TTS.
Juan Manuel Montero,Juana M. Gutiérrez-Arriola,Sira E. Palazuelos,Emilia Enríquez,Santiago Aguilera,José Manuel Pardo +5 more
TL;DR: A through study of emotional speech in Spanish, and its application to TTS, and a prototype system that simulates emotional speech using a commercial synthesiser are presented.
Proceedings ArticleDOI
Emotions in time domain synthesis
B. Heuft,Thomas Portele,M. Rauth +2 more
TL;DR: The outcome of the sawtooth test showed that the amount of information about a speaker's emotional state transported by F/sub 0/, energy and overall duration is rather small, however, one could determine relations between the acoustic prosodic parameters and the emotional content of speech.
Proceedings ArticleDOI
Synthesis of stressed speech from isolated neutral speech using HMM-based models
TL;DR: A novel approach is proposed for modeling speech parameter variations between neutral and stressed conditions and employed in a technique for stressed speech synthesis, and informal listener evaluations of the stress-modified speech confirm the HMM's ability to capture the parameter variations under stressed conditions.