Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?

Schuller, Björn; Wimmer, Matthias; Mösenlechner, Lorenz; Kern, Christian; Arsic, D.; Rigoll, Gerhard

doi:10.1109/icassp.2008.4518656

Cited by 46 publications

(28 citation statements)

References 6 publications

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“…Using this full-size feature set containing all FLDs, we achieved on this two-class classification problem a recognition rate of over 86% on unseen but speaker-dependent data with an SVM classifier. Our classification performance is in the same range as that obtained for comparable tasks-for example, for emotional user state classification (see Batliner et al, 2006;Schuller et al, 2008;Vlasenko et al, 2007).…”

Section: Sleepysupporting

confidence: 62%

Acoustic sleepiness detection: Framework and validation of a speech-adapted pattern recognition approach

Krajewski

Batliner

Gölz

2009

Behavior Research Methods

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Section: Sleepysupporting

confidence: 62%

Acoustic sleepiness detection: Framework and validation of a speech-adapted pattern recognition approach

Krajewski

Batliner

Gölz

2009

Behavior Research Methods

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“…The term 'fragment' will be used in the ongoing referring to a general unit of analysis. Finding the optimal unit of analysis is still an active area of research [28,31,32].…”

Section: The Challenge Of Continuous Emotion Recognitionmentioning

confidence: 99%

On-line emotion recognition in a 3-D activation-valence-time continuum using acoustic and linguistic cues

Eyben

Wöllmer

Graves

et al. 2009

J Multimodal User Interfaces

128

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For many applications of emotion recognition, such as virtual agents, the system must select responses while the user is speaking. This requires reliable on-line recognition of the user's affect. However most emotion recognition systems are based on turnwise processing. We present a novel approach to on-line emotion recognition from speech using Long Short-Term Memory Recurrent Neural Networks. Emotion is recognised frame-wise in a two-dimensional valence-activation continuum. In contrast to current state-of-the-art approaches, recognition is performed on low-level signal frames, similar to those used for speech recognition. No statistical functionals are applied to low-level feature contours. Framing at a higher level is therefore unnecessary and regression outputs can be produced in real-time for every low-level input frame. We also investigate the benefits of including linguistic features on the signal frame level obtained by a keyword spotter.

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“…The term 'segment' will be used in the ongoing referring to a general unit of analysis. Finding the optimal unit of analysis is still an active area of research Schuller et al 2007b;Schuller et al 2008;Busso et al 2007;. As stated in [Zeng et al 2009], the segmentation is one of the most important issues for real applications but has been "largely unexplored so far".…”

Section: The Unit Of Analysismentioning

confidence: 99%

A multitask approach to continuous five-dimensional affect sensing in natural speech

Eyben¹,

Wöllmer²,

Schuller³

2012

ACM Trans. Interact. Intell. Syst.

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Automatic affect recognition is important for the ability of future technical system to interact with us socially in an intelligent way by understanding our current affective state. In recent years there has been a shift in the field of affect recognition from "in the lab" experiments with acted data to "in the wild" experiments with spontaneous and naturalistic data. Two major issues thereby are the proper segmentation of the input and adequate description and modelling of affective states. The first issue is crucial for responsive, real-time systems such as virtual agents and robots, where the latency of the analysis must be as small as possible. To address this issue we introduce a novel method of incremental segmentation to be used in combination with supra-segmental modelling For modelling of continuous affective states we use Long Short-Term Memory Recurrent Neural Networks, with which we can show an improvement in performance over standard recurrent neural networks and feed forward neural networks as well as Support Vector Regression. For experiments we use the SEMAINE database, which contains recordings of spontaneous and natural human to Wizard-of-Oz conversations. The recordings are annotated continuously in time and magnitude with FeelTrace for five affective dimensions, namely activation, expectation, intensity, power/dominance, and valence. To exploit dependencies between the five affective dimensions we investigate multi-task learning of all five dimensions augmented with inter-rater standard deviation. We can show improvements for multi-task over single task modelling. Correlation coefficients of up to 0.81 are obtained for the activation dimension and up to 0.58 for the valence dimension. The performance for the remaining dimensions were found to be in between that for activation and valence.

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Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?

Cited by 46 publications

References 6 publications

Acoustic sleepiness detection: Framework and validation of a speech-adapted pattern recognition approach

Acoustic sleepiness detection: Framework and validation of a speech-adapted pattern recognition approach

On-line emotion recognition in a 3-D activation-valence-time continuum using acoustic and linguistic cues

A multitask approach to continuous five-dimensional affect sensing in natural speech

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