Investigating the role of phoneme-level modifications in emotional speech resynthesis

Bulut, Murtaza; Busso, Carlos; Yıldırım, Serdar; Kazemzadeh, Abe; Lee, Chul Min; Lee, Sungbok; Narayanan, Shrikanth

doi:10.21437/interspeech.2005-378

Cited by 18 publications

(1 citation statement)

References 11 publications

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“…Fragopanagos et al [12] state that most research efforts investigated the affective speech processing on the level of complete utterances, words, or phonetic transcription independent chunks [13,14]. A comparably smaller number of methods are based on phonetic pattern modeling within emotion classification [15,16,17,18,19,20,21,22,23,24,25,26]. Several studies already reported accuracies on multiple corpora -however, only a very few consider training on one and testing on a different one (e.g., [27] and [28], where two and four corpora are employed, respectively).…”

Section: Introductionmentioning

confidence: 99%

Annotators' agreement and spontaneous emotion classification performance

Vlasenko¹,

Wendemuth²

2015

Interspeech 2015

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The combination of various types of data can significantly increase the amount of emotional material for training of more reliable real-life emotion classifiers. There are two well-known schemes of annotation utilized for emotional speech: multidimensional and categories-based. Multi-dimensional annotation is usually applied for labeling spontaneous emotional events, and categorial-based annotation is used for specification of the acted "full blown" emotional chunks. In order to simulate real-life conditions we used a cross-corpora evaluation strategy for datasets with different schemes of emotional annotation. Emotional models were trained on acted material from the EMO-DB (categories based annotation) dataset and evaluated on spontaneous data from the VAM dataset (multidimensional annotation). The best emotion classification performance was obtained on real-life emotional instances with the most intense arousal labels provided by a majority voting strategy (out of 17 annotators). We find that the corresponding spontaneous speech samples containing the most intensive emotional content are comparable with acted instances. The importance of employing a larger number of emotional annotators was finally addressed in our article.

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Section: Introductionmentioning

confidence: 99%

Annotators' agreement and spontaneous emotion classification performance

Vlasenko¹,

Wendemuth²

2015

Interspeech 2015

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Transformation of Emotion by Modifying Prosody and Spectral Energy Using Discrete Wavelet Transform

Geethashree,

D’Souza,

Ravi

2023

Wireless Pers Commun

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DAVID: An open-source platform for real-time emotional speech transformation: With 25 applications in the behavioral sciences

Rachman

Liuni

Arias

et al. 2016

Preprint

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We present an open-source software platform that transforms the emotions expressed by speech signals using audio effects like pitch shifting, inflection, vibrato, and filtering. The emotional transformations can be applied to any audio file, but can also run in real-time (with less than 20-millisecond latency), using live input from a microphone. We anticipate that this tool will be useful for the study of emotions in psychology and neuroscience, because it enables a high level of control over the acoustical and emotional content of experimental stimuli in a variety of laboratory situations, including real-time social situations. We present here results of a series of validation experiments showing that transformed emotions are recognized at above-chance levels in the French, English, Swedish and Japanese languages, with a naturalness comparable to natural speech. Then, we provide a list of twenty-five experimental ideas applying this new tool to important topics in the behavioral sciences.

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Investigating the role of phoneme-level modifications in emotional speech resynthesis

Cited by 18 publications

References 11 publications

Annotators' agreement and spontaneous emotion classification performance

Annotators' agreement and spontaneous emotion classification performance

Transformation of Emotion by Modifying Prosody and Spectral Energy Using Discrete Wavelet Transform

DAVID: An open-source platform for real-time emotional speech transformation: With 25 applications in the behavioral sciences

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