Recognition of affective prosody by speakers of English as a first or foreign language

Dromey, Christopher; Silveira, José Maria Ferreira Jardim da; Sandor, Paul

doi:10.1016/j.specom.2004.09.010

Cited by 20 publications

(22 citation statements)

References 32 publications

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“…The results of a study conducted by Dromey, Silveira, and Sandor (2005) suggest that being a native speaker of a language does not guarantee higher scores on emotion recognition ability in that language. In this study, 32 polyglot L1 English speakers performed marginally better than 57 LX English users, but 53 monoglot L1 English speakers did not outperform LX users in detecting affective prosody at the single word level.…”

Section: Predominance Of a Particular Channel?mentioning

confidence: 98%

Emotion recognition ability in English among L1 and LX users of English

Lorette

Dewaele

2015

IJoLC

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This article focuses on individual differences in emotion recognition ability among 356 first language (L1) and 564 foreign language (LX) users of English. Recognizing emotions can be particularly challenging in LX contexts. Depending on their linguistic profile, individuals may interpret input very differently, and LX learners and users have been found to perform significantly worse than native control groups (Rintell 1984) in tests of emotion recognition ability. In the present article, we investigate the effect of three independent variables, namely, L1 versus LX status, proficiency in English, and cultural background, on emotion recognition ability. We used an online survey in which participants had to identify the emotion portrayed by a native English-speaking actress in six audiovisual clips. Despite LX users having lower proficiency scores, English-L1 users and LX users' emotion recognition ability scores were broadly similar. A significant positive relationship was found between LX proficiency and emotion recognition ability. A similar but only marginally significant relationship emerged among L1 users. A significant effect of L1 culture was found on emotion recognition ability scores, with Asian LX users scoring significantly lower than European LX users. It thus seems that audiovisual input allows advanced LX users to recognize emotions in LX as well as L1 users. That said, LX proficiency and L1 culture do have an effect on emotion recognition ability.

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Section: Predominance Of a Particular Channel?mentioning

confidence: 98%

Emotion recognition ability in English among L1 and LX users of English

Lorette

Dewaele

2015

IJoLC

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“…In the present work, we classify perception studies of affective speech into two categories: one is focused on the level of accurate recognition of affective states in speech (Scherer et al, 2001;Dromey et al, 2005;Thompson et al, 2004), the other undertakes the perceptual evaluation of prosodic features, such as voice quality and prosodic contour, in their ability to convey affective states (Gobl and Ní Chasaide, 2003;Yanushevskaya et al, 2006;Bänziger and Scherer, 2005;Morel and Bänziger, 2004;Chen, 2005;Rodero, 2011). Here, we are principally interested in the latter one.…”

Section: Introductionmentioning

confidence: 99%

The role of voice quality and prosodic contour in affective speech perception

Grichkovtsova¹,

Morel²,

Lacheret³

2012

Speech Communication

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We explore the usage of voice quality and prosodic contour in the identification of emotions and attitudes in French. For this purpose, we develop a corpus of affective speech based on one lexically neutral utterance and apply prosody transplantation method in our perception experiment. We apply logistic regression to analyze our categorical data and we observe differences in the identification of these two affective categories. Listeners primarily use prosodic contour in the identification of studied attitudes. Emotions are identified on the basis of voice quality and prosodic contour. However, their usage is not homogeneous within individual emotions. Depending on the stimuli, listeners may use both voice quality and prosodic contour, or privilege just one of them for the successful identification of emotions. The results of our study are discussed in view of their importance for speech synthesis.

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“…Nevertheless, in many previous studies [3][4][5][6][7][10][11][12], the most popular acoustic features for spoken emotion recognition are prosody features, voice quality features and spectral features. As done in our previous work [31,53], for each emotional utterance, we extracted the aforementioned three typical acoustic features: prosody features, voice quality features and spectral features.…”

Section: Acoustic Feature Extractionmentioning

confidence: 99%

“…As far as feature extraction is concerned, some studies aim at finding the most useful acoustic features related to human emotion expression in speech. The representative acoustic features, used for spoken emotion recognition, contain prosody features [3][4][5][6][7][8][9] such as pitch, intensity, and duration, voice quality features [10][11][12] such as the first three formants (F1, F2, F3), spectral energy distribution, harmonics-to-noise ratio (HNR), as well as spectral features [13][14][15][16][17] such as linear prediction coefficients (LPC), linear prediction cepstral coefficients (LPCC), mel-frequency cepstral coefficients (MFCC). In the aspect of emotion classification, some studies focus on finding various machine learning algorithms to construct a good classifier deciding the underlying emotion categories of speech utterances.…”

Section: Introductionmentioning

confidence: 99%

Spoken emotion recognition via locality-constrained kernel sparse representation

Zhao

Zhang

2014

Neural Comput & Applic

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Spoken emotion recognition is currently a very active research topic and has attracted extensive attention in signal processing, pattern recognition, artificial intelligence, etc. In this paper, a new emotion classification method based on kernel sparse representation, named locality-constrained kernel sparse representation-based classification (LC-KSRC), is proposed for spoken emotion recognition. LC-KSRC is able to learn more discriminating sparse representation coefficients for spoken emotion recognition, since it integrates both sparsity and data locality in the kernel feature space. The proposed method is compared with six representative emotion classification methods, including linear discriminant classifier, K-nearestneighbor, radial basis function neural networks, support vector machines, sparse representation-based classification and kernel sparse representation-based classification. Experimental results on two publicly available emotional speech databases, i.e., the Berlin database and the Polish database, demonstrate the promising performance of the proposed method on spoken emotion recognition tasks, outperforming the other used methods.

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Recognition of affective prosody by speakers of English as a first or foreign language

Cited by 20 publications

References 32 publications

Emotion recognition ability in English among L1 and LX users of English

Emotion recognition ability in English among L1 and LX users of English

The role of voice quality and prosodic contour in affective speech perception

Spoken emotion recognition via locality-constrained kernel sparse representation

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