Emotions, speech and the ASR framework

Bosch, L.F.M. ten

doi:10.1016/s0167-6393(02)00083-3

Cited by 83 publications

(31 citation statements)

References 44 publications

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“…The reason why is probably that elaborated tools such as HTK have been previously developed for similar tasks such as speech and speaker recognition. For acted emotions, there are numerous references [215,188,249]; for non-acted emotions, fewer are known [105,225,231,193]: The performance of static modelling through functionals is often reported as superior [188,225,193] as emotion is apparently better modelled on a time-scale above frame-level; note that a combination of static features such as minimum, maximum, onset, offset, duration, regression, etc. implicitly shape contour dynamics as well.…”

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

614

329

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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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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

614

329

View full text Add to dashboard Cite

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“…In fact, existing automatic speech recognition systems can benefit from the extra information that emotion recognition can provide (Ten Bosch, 2003;Dusan and Rabiner, 2005). It would be useful to produce speech transcripts that not only contain the words said by different speakers, but also the speaker's state or emotion under which the words were said.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of the robustness of existing supervised machine learning approaches to the classification of emotions in speech

Shami

Verhelst

2007

Speech Communication

111

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In this study, the robustness of approaches to the automatic classification of emotions in speech is addressed. Among the many types of emotions that exist, two groups of emotions are considered, adult-to-adult acted vocal expressions of common types of emotions like happiness, sadness, and anger and adult-to-infant vocal expressions of affective intents also known as "motherese". Specifically, we estimate the generalization capability of two feature extraction approaches, the approach developed for Sony's robotic dog AIBO (AIBO) and the segment-based approach (SBA) of . Three machine learning approaches are considered, K-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Adaboosted decision trees and four emotional speech databases are employed, Kismet, BabyEars, Danish, and Berlin databases.Single corpus experiments show that the considered feature extraction approaches AIBO and SBA are competitive on the four databases considered and that their performance is comparable with previously published results on the same databases. The best choice of machine learning algorithm seems to depend on the feature extraction approach considered.Multi corpus experiments are performed with the Kismet-BabyEars and the Danish-Berlin database pairs that contain parallel emotional classes. Automatic clustering of the emotional classes in the database pairs shows that the patterns behind the emotions in the Kismet-BabyEars pair are less database dependent than the patterns in the Danish-Berlin pair. In off-corpus testing the classifier is trained on one database of a pair and tested on the other. This provides little improvement over baseline classification. In integrated corpus testing, however, the classifier is machine learned on the merged databases and this gives promisingly robust classification results, which suggest that emotional corpora with parallel emotion classes recorded under different conditions can be used to construct a single classifier capable of distinguishing the emotions in the merged corpora. Such a classifier is more robust than a classifier learned on a single corpus as it can recognize more varied expressions of the same emotional classes. These findings suggest that the existing approaches for the classification of emotions in speech are efficient enough to handle larger amounts of training data without any reduction in classification accuracy.

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“…Komad ongi eesti keeles sellised kirjavahemärgid, mille kohal jäetakse paus tihtipeale tegemata. Ka mõned varasemad uurimused kinnitavad sama (vt Tamuri 2007, Pajupuu, Kerge 2006 Uurimismaterjalis oli emotsiooniti komasid lausetes enam-vähem võrdselt: kurbuselausetes 72, rõõmulausetes 67 ja vihalausetes 80. Koma kohal tegi lugeja kõige vähem pause kurbuselausetes -umbes pooltel juhtudel.…”

Section: Uurimisküsimused Ja -Materjalunclassified

Kas pausid kannavad emotsiooni?

Tamuri

2010

ERYa

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Ülevaade. Pausidele on emotsionaalse kõne uurimustes suhteliselt vähe tähelepanu pööratud. Ilmselt seetõttu, et pausid ei ole esmased emotsiooni kandvad akustilised tunnused. Siiski on teada, et pausidel on emotsioonide eristamisel oma roll. Artiklis antakse vastus kahele küsimusele: kas eestikeelse etteloetud emotsionaalse teksti pausid sõltuvad oluliselt teksti emotsioonist ning kas teksti emotsioon on ära tuntav ainult pausierinevuste põhjal.* Võtmesõnad: emotsioonide akustika, etteloetud tekst, pausigrupid, pauside arv, pauside asukoht, pauside iseloom, pauside kestus, kõne-süntees, eesti keel

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Emotions, speech and the ASR framework

Cited by 83 publications

References 44 publications

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

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

An evaluation of the robustness of existing supervised machine learning approaches to the classification of emotions in speech

Kas pausid kannavad emotsiooni?

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