Emotion Perception and Recognition from Speech

Wu, Chung Hsien; Yeh, Jui Feng; Chuang, Ze Jing

doi:10.1007/978-1-84800-306-4_6

Cited by 19 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Pitch, intensity, duration, voice quality, and long term spectra are gathered under the umbrella of supra-segmental or prosodic features [77,90,99,96,140,166,92,241].…”

Section: Acoustic Low-level Descriptorsmentioning

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

615

329

View full text Add to dashboard Cite

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).

show abstract

“…Pitch, intensity, duration, voice quality, and long term spectra are gathered under the umbrella of supra-segmental or prosodic features [77,90,99,96,140,166,92,241].…”

Section: Acoustic Low-level Descriptorsmentioning

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

615

329

View full text Add to dashboard Cite

show abstract

“…We choose a commercial product in LVA instead of constructing emotion metrics from vocal acoustic features directly because it is not clear from the literature which vocal emotion measurement model would be most appropriate. The literature on identifying which acoustic features to extract from voice and how to combine them for affective state classification is vast and evolving, with little agreement on which models are superior (Ververidis and Kotropoulos (2006), Wu, Yeh, and Chuang (2009), Schuller (2010), Yang and Lugger (2010)). Naturally, using a commercial product like LVA is also limiting because the developers are reluctant to divulge the specific acoustic features they extract from voice and how they combine these features.…”

Section: Measuring Nonverbal Communicationmentioning

confidence: 99%

The Power of Voice: Managerial Affective States and Future Firm Performance

Mayew¹,

Venkatachalam

2012

The Journal of Finance

482

202

View full text Add to dashboard Cite

We measure managerial affective states during earnings conference calls by analyzing conference call audio files using vocal emotion analysis software. We hypothesize and find that, when managers are scrutinized by analysts during conference calls, positive and negative affects displayed by managers are informative about the firm's financial future. Analysts do not incorporate this information when forecasting near-term earnings. When making stock recommendation changes, however, analysts incorporate positive but not negative affect. This study presents new evidence that managerial vocal cues contain useful information about a firm's fundamentals, incremental to both quantitative earnings information and qualitative "soft" information conveyed by linguistic content.It is not what you say that matters but the manner in which you say it; there lies the secret of the ages.-

show abstract

“…Numerous features such as prosodic and acoustic features of emotional speech signals have been discussed over the years [78][79][80][81][82]. Numerous features such as prosodic and acoustic features of emotional speech signals have been discussed over the years [78][79][80][81][82].…”

Section: A) Audio Featuresmentioning

confidence: 99%

“…An important issue for emotion recognition from speech is the selection of salient features. Numerous features such as prosodic and acoustic features of emotional speech signals have been discussed over the years [78][79][80][81][82]. Among these features, prosodic features have been found to represent the most significant characteristics of emotional content in verbal communication and were widely and successfully used for speech emotion recognition [12,83,84].…”

Section: A) Audio Featuresmentioning

confidence: 99%

Survey on audiovisual emotion recognition: databases, features, and data fusion strategies

Lin

Wei

2014

SIP

Self Cite

145

View full text Add to dashboard Cite

Emotion recognition is the ability to identify what people would think someone is feeling from moment to moment and understand the connection between his/her feelings and expressions. In today's world, human-computer interaction (HCI) interface undoubtedly plays an important role in our daily life. Toward harmonious HCI interface, automated analysis and recognition of human emotion has attracted increasing attention from the researchers in multidisciplinary research fields. In this paper, a survey on the theoretical and practical work offering new and broad views of the latest research in emotion recognition from bimodal information including facial and vocal expressions is provided. First, the currently available audiovisual emotion databases are described. Facial and vocal features and audiovisual bimodal data fusion methods for emotion recognition are then surveyed and discussed. Specifically, this survey also covers the recent emotion challenges in several conferences. Conclusions outline and address some of the existing emotion recognition issues.

show abstract

Emotion Perception and Recognition from Speech

Cited by 19 publications

References 30 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

The Power of Voice: Managerial Affective States and Future Firm Performance

Survey on audiovisual emotion recognition: databases, features, and data fusion strategies

Contact Info

Product

Resources

About