Combining speech recognition and acoustic word emotion models for robust text-independent emotion recognition

Schuller, Björn; Vlasenko, Bogdan; Arsic, D.; Rigoll, Gerhard; Wendemuth, Andreas

doi:10.1109/icme.2008.4607689

Cited by 21 publications

(5 citation statements)

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“…Similar observations have been reported by Schuller et al [14]. In their experiments, they trained multiple emotion classification models on phoneme and word level segments.…”

Section: Related Worksupporting

confidence: 82%

Infected Phonemes: How a Cold Impairs Speech on a Phonetic Level

Wagner

Fraga-Silva

Josse³

et al. 2017

Interspeech 2017

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The realization of language through vocal sounds involves a complex interplay between the lungs, the vocal cords, and a series of resonant chambers (e.g. mouth and nasal cavities). Due to their connection to the outside world, these body parts are popular spots for viruses and bacteria to enter the human organism. Affected people may suffer from an upper respiratory tract infection (URTIC) and consequently their voice often sounds breathy, raspy or sniffly. In this paper, we investigate the audible effects of a cold on a phonetic level. Results on a German corpus show that the articulation of consonants is more impaired than that of vowels. Surprisingly, nasal sounds do not follow this trend in our experiments. We finally try to predict a speaker's health condition by fusing decisions we derive from single phonemes. The presented work is part of the INTER-

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“…Similar observations have been reported by Schuller et al [14]. In their experiments, they trained multiple emotion classification models on phoneme and word level segments.…”

Section: Related Worksupporting

confidence: 82%

Infected Phonemes: How a Cold Impairs Speech on a Phonetic Level

Wagner

Fraga-Silva

Josse³

et al. 2017

Interspeech 2017

View full text Add to dashboard Cite

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“…A possibility to use static classifiers for frame-level feature processing is further given by multi-instance learning techniques, where a time series of unknown length is handled as one by SVM or similar techniques [206,187]. Still, when the spoken content is fixed, the combination of static and dynamic processing may help improve overall accuracy [224,195].…”

Section: Classificationmentioning

confidence: 99%

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Schuller

Batliner

Steidl

et al. 2011

Speech Communication

603

340

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More than a decade has passed since research on automatic recognition of emotion from speech has become a new field of research in line with its 'big brothers' speech and speaker recognition. This article attempts to provide a short overview on where we are today, how we got there and what this can reveal us on where to go next and how we could arrive there. In a first part, we address the basic phenomenon reflecting the last fifteen years, commenting on databases, modelling and annotation, the unit of analysis and prototypicality. We then shift to automatic processing including discussions on features, classification, robustness, evaluation, and implementation and system integration. From there we go to the first comparative challenge on emotion recognition from speech -the INTERSPEECH 2009 Emotion Challenge, organised by (part of) the authors, including the description of the Challenge's database, Sub-Challenges, participants and their approaches, the winners, and the fusion of results to the actual learnt lessons before we finally address the ever-lasting problems and future promising attempts.Keywords: emotion, affect, automatic classification, feature types, feature selection, noise robustness, adaptation, standardisation, usability, evaluation Setting the SceneThis special issue will address new approaches towards dealing with the processing of realistic emotions in speech, and this overview article will give an account of the state-of-the-art, of the lacunas in this field, and of promising approaches towards overcoming shortcomings in modelling and recognising realistic emotions. We will also report on the first emotion challenge at INTERSPEECH 2009, constituting the initial impetus of this special issue; to end with, we want to sketch future strategies and applications, trying to answer the question 'Where to go from here?'The article is structured as follows: we first deal with the basic phenomenon briefly reflecting the last fifteen years, commenting on databases, modelling and annotation, the unit of analysis and prototypicality. We then proceed to automatic processing (sec. 2) including discussions on features, classification, robustness, evaluation, and implementation and system integration. From there we go to the the first Emotion Challenge (sec. 3) including the description of the Challenge's database, Sub-Challenges, participants and their approaches, the winners, and the fusion of results to the lessons learnt, before concluding this article (sec. 4).

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“…The mean value of overlap has been fixed to 15% of the speech frames for the overall dataset. For each sentence the amount of overlap is obtained as a random value drown from the uniform distribution on the interval [12,18]. This assumption allows the artificial database to reflect the frequency of overlapped speech in real-life scenarios such as two-party telephone conversation or meeting (Shriberg et al (2000)).…”

Section: Corpus Descriptionmentioning

confidence: 99%

Speech Enhancement, Modeling and Recognition- Algorithms and Applications

Swaminathan¹

2012

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Combining speech recognition and acoustic word emotion models for robust text-independent emotion recognition

Cited by 21 publications

References 4 publications

Infected Phonemes: How a Cold Impairs Speech on a Phonetic Level

Infected Phonemes: How a Cold Impairs Speech on a Phonetic Level

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Speech Enhancement, Modeling and Recognition- Algorithms and Applications

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