An Open Source Emotional Speech Corpus for Human Robot Interaction Applications

James, Jesin; Tian, Li; Watson, Catherine

doi:10.21437/interspeech.2018-1349

Cited by 24 publications

(21 citation statements)

References 20 publications

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“…20 subjects participated in this evaluation task. James et al developed an open-source speech corpus [19] for 5 primary emotions and 5 secondary emotions consisting of total 2400 sentences for the New Zealand English. There were 120 participants for the subject evaluation of the corpus.…”

Section: Related Workmentioning

confidence: 99%

SUST Bangla Emotional Speech Corpus (SUBESCO): An audio-only emotional speech corpus for Bangla

et al. 2021

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SUBESCO is an audio-only emotional speech corpus for Bangla language. The total duration of the corpus is in excess of 7 hours containing 7000 utterances, and it is the largest emotional speech corpus available for this language. Twenty native speakers participated in the gender-balanced set, each recording of 10 sentences simulating seven targeted emotions. Fifty university students participated in the evaluation of this corpus. Each audio clip of this corpus, except those of Disgust emotion, was validated four times by male and female raters. Raw hit rates and unbiased rates were calculated producing scores above chance level of responses. Overall recognition rate was reported to be above 70% for human perception tests. Kappa statistics and intra-class correlation coefficient scores indicated high-level of inter-rater reliability and consistency of this corpus evaluation. SUBESCO is an Open Access database, licensed under Creative Common Attribution 4.0 International, and can be downloaded free of charge from the web link: https://doi.org/10.5281/zenodo.4526477.

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Section: Related Workmentioning

confidence: 99%

SUST Bangla Emotional Speech Corpus (SUBESCO): An audio-only emotional speech corpus for Bangla

et al. 2021

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“…The Surrey Audio-Visual Expressed Emotion (SAVEE) [141] database covers 14 speakers with 7 different emotions but only contains 480 utterances in total. JL-Corpus [142] is a collection of 4 New-Zealand English speakers uttering 15 sentences in 5 primary emotions with 2 repetitions, and another 10 sentences in 5 secondary emotions. In general, these databases are all of good quality and can be ideal to build a rule-based emotional voice conversion framework.…”

Section: Lexical Variabilitymentioning

confidence: 99%

Emotional Voice Conversion: Theory, Databases and ESD

Zhou¹,

Şişman²,

Li³

et al. 2021

Preprint

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In this paper, we first provide a review of the state-of-the-art emotional voice conversion research, and the existing emotional speech databases. We then motivate the development of a novel emotional speech database (ESD) that addresses the increasing research need. With this paper, the ESD database 1 is now made available to the research community. The ESD database consists of 350 parallel utterances spoken by 10 native English and 10 native Chinese speakers and covers 5 emotion categories (neutral, happy, angry, sad and surprise). More than 29 hours of speech data were recorded in a controlled acoustic environment. The database is suitable for multi-speaker and cross-lingual emotional voice conversion studies. As case studies, we implement several state-of-the-art emotional voice conversion systems on the ESD database. This paper provides a reference study on ESD in conjunction with its release.

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“…The ability of the regressor to differentiate between emotions resp. place the emotions in the AV space was tested on ten publicly available databases: EmoDB [19], EMOVO [20], RAVDESS [21], CREMA-D [22], SAVEE [23], VESUS [24], eNTERFACE [25], JL Corpus [26], TESS [27], and GEES [28]. These databases are categorically annotated and do not include information on AV values.…”

Section: Testing Databasesmentioning

confidence: 99%

Mapping Discrete Emotions in the Dimensional Space: An Acoustic Approach

et al. 2021

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A frequently used procedure to examine the relationship between categorical and dimensional descriptions of emotions is to ask subjects to place verbal expressions representing emotions in a continuous multidimensional emotional space. This work chooses a different approach. It aims at creating a system predicting the values of Activation and Valence (AV) directly from the sound of emotional speech utterances without the use of its semantic content or any other additional information. The system uses X-vectors to represent sound characteristics of the utterance and Support Vector Regressor for the estimation the AV values. The system is trained on a pool of three publicly available databases with dimensional annotation of emotions. The quality of regression is evaluated on the test sets of the same databases. Mapping of categorical emotions to the dimensional space is tested on another pool of eight categorically annotated databases. The aim of the work was to test whether in each unseen database the predicted values of Valence and Activation will place emotion-tagged utterances in the AV space in accordance with expectations based on Russell’s circumplex model of affective space. Due to the great variability of speech data, clusters of emotions create overlapping clouds. Their average location can be represented by centroids. A hypothesis on the position of these centroids is formulated and evaluated. The system’s ability to separate the emotions is evaluated by measuring the distance of the centroids. It can be concluded that the system works as expected and the positions of the clusters follow the hypothesized rules. Although the variance in individual measurements is still very high and the overlap of emotion clusters is large, it can be stated that the AV coordinates predicted by the system lead to an observable separation of the emotions in accordance with the hypothesis. Knowledge from training databases can therefore be used to predict AV coordinates of unseen data of various origins. This could be used to detect high levels of stress or depression. With the appearance of more dimensionally annotated training data, the systems predicting emotional dimensions from speech sound will become more robust and usable in practical applications in call-centers, avatars, robots, information-providing systems, security applications, and the like.

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An Open Source Emotional Speech Corpus for Human Robot Interaction Applications

Cited by 24 publications

References 20 publications

SUST Bangla Emotional Speech Corpus (SUBESCO): An audio-only emotional speech corpus for Bangla

SUST Bangla Emotional Speech Corpus (SUBESCO): An audio-only emotional speech corpus for Bangla

Emotional Voice Conversion: Theory, Databases and ESD

Mapping Discrete Emotions in the Dimensional Space: An Acoustic Approach

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