Emotion Recognition from Speech by Combining Databases and Fusion of Classifiers

Lefter, Iulia; Rothkrantz, Léon J. M.; Wiggers, Pascal; Leeuwen, David A. van

doi:10.1007/978-3-642-15760-8_45

Cited by 35 publications

(27 citation statements)

References 19 publications

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“…The audio feature set consists of 30 features inspired from [4]: speech duration, statistics (mean, standard deviation, slope, range) on pitch (F0) and intensity, mean formants F1-F4 and their bandwidth, jitter, shimmer, high frequency energy, HNR, Hammarberg index, center of gravity and skewness of the spectrum. These features are computed on segments of length equal to 2 seconds, because this resembles better what we can expect in real-time processing.…”

Section: Acoustic Featuresmentioning

confidence: 99%

Aggression Detection in Speech Using Sensor and Semantic Information

Lefter

Rothkrantz

Burghouts

2012

Text, Speech and Dialogue

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Abstract. By analyzing a multimodal (audio-visual) database with aggressive incidents in trains, we have observed that there are no trivial fusion algorithms to successfully predict multimodal aggression based on unimodal sensor inputs. We proposed a fusion framework that contains a set of intermediate level variables (meta-features) between the low level sensor features and the multimodal aggression detection [1]. In this paper we predict the multimodal level of aggression and two of the meta-features: context and semantics. We do this based on the audio stream, from which we extract both nonverbal and verbal information. Given the spontaneus nature of speech in the database, we rely on a keyword spotting approach in the case of verbal information. We have found the existence of 6 semantic groups of keywords that have a positive influence on the prediction of aggression and of the two meta-features.

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Section: Acoustic Featuresmentioning

confidence: 99%

Aggression Detection in Speech Using Sensor and Semantic Information

Lefter

Rothkrantz

Burghouts

2012

Text, Speech and Dialogue

Self Cite

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“…Another issue when using publicly available data sets is how to effectively combine them. Lefter et al [22] presented an approach where combinations of several datasets for training and testing were used. They examined the case where evaluation took place in unseen datasets and showed that in that case and the performance was significantly low, although there were a few exceptions.…”

Section: Related Workmentioning

confidence: 99%

Deep Visual Attributes vs. Hand-Crafted Audio Features on Multidomain Speech Emotion Recognition

et al. 2017

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Emotion recognition from speech may play a crucial role in many applications related to human-computer interaction or understanding the affective state of users in certain tasks, where other modalities such as video or physiological parameters are unavailable. In general, a human's emotions may be recognized using several modalities such as analyzing facial expressions, speech, physiological parameters (e.g., electroencephalograms, electrocardiograms) etc. However, measuring of these modalities may be difficult, obtrusive or require expensive hardware. In that context, speech may be the best alternative modality in many practical applications. In this work we present an approach that uses a Convolutional Neural Network (CNN) functioning as a visual feature extractor and trained using raw speech information. In contrast to traditional machine learning approaches, CNNs are responsible for identifying the important features of the input thus, making the need of hand-crafted feature engineering optional in many tasks. In this paper no extra features are required other than the spectrogram representations and hand-crafted features were only extracted for validation purposes of our method. Moreover, it does not require any linguistic model and is not specific to any particular language. We compare the proposed approach using cross-language datasets and demonstrate that it is able to provide superior results vs. traditional ones that use hand-crafted features.

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“…In most cases, papers refer to z-normalisation (cf. [7,9,16,21,22,25]) and further, to mean-variance-normalisation (cf. [29]).…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Normalisation in Emotion Recognition from Speech

Böck

Egorow

Siegert

et al. 2017

Intelligent Human Computer Interaction

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Abstract. The recognition performance of a classifier is affected by various aspects. A huge influence is given by the input data pre-processing. In the current paper we analysed the relation between different normalisation methods for emotionally coloured speech samples deriving general trends to be considered during data pre-processing. From the best of our knowledge, various normalisation approaches are used in the spoken affect recognition community but so far no multi-corpus comparison was conducted. Therefore, well-known methods from literature were compared in a larger study based on nine benchmark corpora, where within each data set a leave-one-speaker-out validation strategy was applied. As normalisation approaches, we investigated standardisation, range normalisation, and centering. These were tested in two possible options: (1) The normalisation parameters were estimated on the whole data set and (2) we obtained the parameters by using emotionally neutral samples only. For classification Support Vector Machines with linear and polynomial kernels as well as Random Forest were used as representatives of classifiers handling input material in different ways. Besides further recommendations we showed that standardisation leads to a significant improvement of the recognition performance. It is also discussed when and how to apply normalisation methods.

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Emotion Recognition from Speech by Combining Databases and Fusion of Classifiers

Cited by 35 publications

References 19 publications

Aggression Detection in Speech Using Sensor and Semantic Information

Aggression Detection in Speech Using Sensor and Semantic Information

Deep Visual Attributes vs. Hand-Crafted Audio Features on Multidomain Speech Emotion Recognition

Comparative Study on Normalisation in Emotion Recognition from Speech

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