The Vera am Mittag German audio-visual emotional speech database

Grimm, Michael; Kroschel, Kristian; Narayanan, Shrikanth

doi:10.1109/icme.2008.4607572

Cited by 320 publications

(192 citation statements)

References 4 publications

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“…Stimuli were short audio-only utterances from an authentic and affectively-colored German conversational speech corpus (Grimm et al 2008). Emotion inferences from speech correlate across languages, particularly for similar languages (cf.…”

Section: Task and Proceduresmentioning

confidence: 99%

Do Hearing Aids Improve Affect Perception?

Schmidt

Herzog

Scharenborg³

et al. 2016

Advances in Experimental Medicine and Biology

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Normal-hearing listeners use acoustic cues in speech to interpret a speaker's emotional state. This study investigates the effect of hearing aids on the perception of the emotion dimensions arousal (aroused/calm) and valence (positive/ negative attitude) in older adults with hearing loss. More specifically, we investigate whether wearing a hearing aid improves the correlation between affect ratings and affect-related acoustic parameters. To that end, affect ratings by 23 hearing-aid users were compared for aided and unaided listening. Moreover, these ratings were compared to the ratings by an age-matched group of 22 participants with age-normal hearing.For arousal, hearing-aid users rated utterances as generally more aroused in the aided than in the unaided condition. Intensity differences were the strongest indictor of degree of arousal. Among the hearing-aid users, those with poorer hearing used additional prosodic cues (i.e., tempo and pitch) for their arousal ratings, compared to those with relatively good hearing. For valence, pitch was the only acoustic cue that was associated with valence. Neither listening condition nor hearing loss severity (differences among the hearing-aid users) influenced affect ratings or the use of affect-related acoustic parameters. Compared to the normal-hearing reference group, ratings of hearing-aid users in the aided condition did not generally differ in both emotion dimensions. However, hearing-aid users were more sensitive to intensity differences in their arousal ratings than the normal-hearing participants.We conclude that the use of hearing aids is important for the rehabilitation of affect perception and particularly influences the interpretation of arousal.

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Section: Task and Proceduresmentioning

confidence: 99%

Do Hearing Aids Improve Affect Perception?

Schmidt

Herzog

Scharenborg³

et al. 2016

Advances in Experimental Medicine and Biology

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show abstract

“…Due to the subjectivity of emotion many state-of-the-art corpora are rated by a number of labellers and the label is determined from the various ratings given by the labellers. In some cases a measure of agreement or confidence can also be given-for example, in the case of dimensional annotation an average of the labeller's rating values can be used as a consensus label and the standard deviation of ratings can be used as an agreement measure [18].…”

Section: Classifying Emotion In Speechmentioning

confidence: 99%

“…1) Vera am Mittag German Audio-Visual Emotional Speech Database is a natural corpus of German talkshow recordings [18] which contains assets rated on three dimensional scales on a scale of -1 to +1. The dimensions were activation, valence (a synonym for evaluation) and dominance (a synonym for power).…”

Section: Datasets Usedmentioning

confidence: 99%

“…Speech corpora that are used in emotion recognition from speech can be separated into three groups: natural speech (using data from call-centers [10], talk-shows [18] and similar sources where people exhibit natural emotions), acted speech (where actors are asked to portray specific emotions [6]) and elicited speech (subjects are placed in a controlled environment [3] and emotions are induced by changing their environment). Acted speech should not be used in the training of a classifier for spontaneous, real-life emotions [7] and for this reason this study concentrates only on natural or elicited corpora.…”

Section: Classifying Emotion In Speechmentioning

confidence: 99%

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Benchmarking classification models for emotion recognition in natural speech: A multi-corporal study

Тарасов¹,

Delany²

2011

Face and Gesture 2011

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Abstract-A significant amount of the research on automatic emotion recognition from speech focuses on acted speech that is produced by professional actors. This approach often leads to overoptimistic results as the recognition of emotion in real-life conditions is more challenging due the propensity of mixed and less intense emotions in natural speech. The paper presents an empirical study of the most widely used classifiers in the domain of emotion recognition from speech, across multiple non-acted emotional speech corpora. The results indicate that Support Vector Machines have the best performance and that they along with Multi-Layer Perceptron networks and k-nearest neighbour classifiers perform significantly better (using the appropriate statistical tests) than decision trees, Naïve Bayes classifiers and Radial Basis Function networks.

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“…The method used for speech recognition system in this research, focuses on Marathi Language & it is important to see that whether Speech Recognition System for Marathi can be carried out similar pathways of research as carried out in English. [2,3] The paper is divided in to five sections, Section 1, gives Introduction, Section 2 deals with Marathi Speech Database, section 3 focuses on feature extraction techniques, section 4 focuses on DTW Pattern Matching technique ,Section 5 covers Results & conclusion followed by Section 6 gives the valuable References. To achieve the good quality of audio-results, recording of the sentences by speakers is done in college computer laboratory, after completion of college work to avoid noisy surrounding.…”

Section: Introductionmentioning

confidence: 99%

Isolated Word Recognition System based on LPC and DTW Technique

Pawar¹

2012

IJCA

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The Voice is a signal of infinite information. Digital processing of speech signal is very important for high and precise automatic voice recognition technology. Speech recognition has found its application on various aspects of our daily lives from automatic phone answering service to dictating text and issuing voice commands to computers. In this paper, we present the steps involved in the design of a speaker-independent speech recognition system for Marathi Language. We focus mainly on the pre-processing stage that extracts salient features of a speech signal and a technique called Dynamic Time Warping commonly used to compare the feature vectors of speech signals. These techniques are applied for recognition of isolated as well as connected words spoken. In his case the experiment is conducted in MATLAB to verify these techniques.

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The Vera am Mittag German audio-visual emotional speech database

Cited by 320 publications

References 4 publications

Do Hearing Aids Improve Affect Perception?

Do Hearing Aids Improve Affect Perception?

Benchmarking classification models for emotion recognition in natural speech: A multi-corporal study

Isolated Word Recognition System based on LPC and DTW Technique

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