Data Mining Practical Machine Learning Tools and Techniques

The article describes a database of emotional speech. Ten actors (5 female and 5 male) simulated the emotions, producing 10 German utterances (5 short and 5 longer sentences) which could be used in everyday communication and are interpretable in all applied emotions. The recordings were taken in an anechoic chamber with high-quality recording equipment. In addition to the sound electro-glottograms were recorded. The speech material comprises about 800 sentences (seven emotions * ten actors * ten sentences + some second versions). The complete database was evaluated in a perception test regarding the recognisability of emotions and their naturalness. Utterances recognised better than 80% and judged as natural by more than 60% of the listeners were phonetically labelled in a narrow transcription with special markers for voice-quality, phonatory and articulatory settings and articulatory features. The database can be accessed by the public via the internet (http://www.expressive-speech.net/emodb/).

/pdf/a-database-of-german-emotional-speech-2utsa757sb.pdf

A database of German emotional speech.

Survey on speech emotion recognition: Features, classification schemes, and databases

http://www.cs.columbia.edu/~julia/papers/Scherer03.pdf

Vocal communication of emotion: a review of research paradigms

Many authors have speculated about a close relationship between vocal expression of emotions and musical expression of emotions. but evidence bearing on this relationship has unfortunately been lacking. This review of 104 studies of vocal expression and 41 studies of music performance reveals similarities between the 2 channels concerning (a) the accuracy with which discrete emotions were communicated to listeners and (b) the emotion-specific patterns of acoustic cues used to communicate each emotion. The patterns are generally consistent with K. R. Scherer's (1986) theoretical predictions. The results can explain why music is perceived as expressive of emotion, and they are consistent with an evolutionary perspective on vocal expression of emotions. Discussion focuses on theoretical accounts and directions for future research.

/pdf/communication-of-emotions-in-vocal-expression-and-music-2to7fzkqnc.pdf

Communication of emotions in vocal expression and music performance: different channels, same code?

Felix Burkhardt*, Markus van Ballegooy*, Roman Englert**, Richard Huber***T-Systems International GmbH*, Deutsche Telekom Laboratories**, Sympalog Voice Solutions GmbH***Berlin / Erlangen, Germany{felix.burkhardt|markus.van-ballegooy}@t-systems.com,roman.englert@telekom.de, huber@sympalog.deAbstractThis article describes the development of a voice portal prototype that integrates the recog-nition of anger in the caller’s voice. Often users get frustrated by talking to machines and hangup without having the possibility to express their anger in some way. In order to handle thisproblem, we integrated an emotional recognizer in a standard-based voice portalIf anger is detected in the caller’s voice, the dialog manager component runs a conciliationstrategy by ﬁrst mirroring the caller’s mood and in a second step transferring the caller to ahuman agent.The system design is described and ﬁrst evaluation results reported.

An Emotion-Aware Voice Portal

We researched how “likable” or “pleasant” a speaker appears based on a subset of the “Agender” database which was recently introduced at the 2010 Interspeech Paralinguistic Challenge. 32 participants rated the stimuli according to their likability on a seven point scale. An Anova showed that the samples rated are significantly different although the inter-rater agreement is not very high. Experiments with automatic regression and classification by REPTree ensemble learning resulted in a crosscorrelation of up to .378 with the evaluator weighted estimator, and 67.6 % accuracy in binary classification (likable / not likable). Analysis of individual acoustic feature groups reveals that for this data, auditory spectral features seem to contribute the most to reliable automatic likability analysis.

"Would You Buy a Car from Me?" - On the Likability of Telephone Voices.

Emofilt is a software program intended to simulate emotional arousal with speech synthesis based on the free-for-noncommercial-use MBROLA synthesis engine. It acts as a transformer between the phonetisation and the speech-generation component. Originally developed at the Technical University of Berlin it was recently revived as an open-source project written in Java (http://emofilt.sourceforge.net). Emofilt’s languagedependent modules are controlled by external XML-files and it is as multilingual as MBROLA which currently supports 35 languages. It might be used for research, teaching or to implement applications that include the simulation of emotional speech.

http://felix.syntheticspeech.de/publications/emofiltInterspeech05.pdf

Emofilt: the simulation of emotional speech by prosody-transformation.

Data sparseness is an ever dominating problem in automatic emotion recognition. Using artificially generated speech for training or adapting models could potentially ease this: though less natural than human speech, one could synthesize the exact spoken content in different emotional nuances - of many speakers and even in different languages. To investigate chances, the phonemisation components Txt2Pho and openMary are used with Emofilt and Mbrola for emotional speech synthesis. Analysis is realized with our Munich open Emotion and Affect Recognition toolkit. As test set we gently limit to the acted Berlin and eNTERFACE databases for the moment. In the result synthesized speech can indeed be used for the recognition of human emotional speech.

/pdf/learning-with-synthesized-speech-for-automatic-emotion-250of7y2r2.pdf

Learning with synthesized speech for automatic emotion recognition

Emotion plays an important role in human communication and therefore also human machine dialog systems can benefit from affective processing. We present in this paper an overview of our work from the past few years and discuss general considerations, potential applications and experiments that we did with the emotional classification of human machine dialogs. Anger in voice portals as well as problematic dialog situations can be detected to some degree, but the noise in real life data and the issue of unambiguous emotion definition are still challenging. Also, a dialog system reacting emotionally might raise expectations with respect to its intellectual abilities that it can not fulfill.

/pdf/emotion-detection-in-dialog-systems-applications-strategies-3rvp6h1908.pdf

Felix Burkhardt

Papers

An Emotion-Aware Voice Portal

"Would You Buy a Car from Me?" - On the Likability of Telephone Voices.

Emofilt: the simulation of emotional speech by prosody-transformation.

Learning with synthesized speech for automatic emotion recognition

Emotion detection in dialog systems: Applications, strategies and challenges