There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Note on Translation Introduction by Michael Holquist Response to a Question from the Novy Mir Editorial Staff The Bildungsroman and Its Significance in the History of Realism (Toward a Historical Typology of the Novel) The Problem of Speech Genres The Problem of the Text in Linguistics, Philology, and the Human Sciences: An Experiment in Philosophical Analysis From Notes Made in 1970-71 Toward a Methodology for the Human Sciences Index

Speech Genres and Other Late Essays

https://dspace.stir.ac.uk/bitstream/1893/25490/1/affective-computing-review.pdf

A review of affective computing

On a role

To enable a naturalistic human–computer interaction the recognition of emotions and intentions experiences increased attention and several modalities are comprised to cover all human communication abilities. For this reason, naturalistic material is recorded, where the subjects are guided through an interaction with crucial points, but with the freedom to react individually. This material captures realistic user reactions but lacks of clear labels. So, a good transcription and annotation of the given material is essential. For that, the assignment of human annotators has become widely accepted. A good measurement for the reliability of labelled material is the inter-rater agreement. In this paper we investigate the achieved inter-rater agreement utilizing Krippendorff’s alpha for emotional annotated interaction corpora and present methods to improve the reliability, we show that the reliabilities obtained with different methods does not differ much, so a choice could rely on other aspects. Furthermore, a multimodal presentation of the items in their natural order increases the reliability.

Inter-rater reliability for emotion annotation in human–computer interaction: comparison and methodological improvements

Spoken language is one of the main interaction patterns in human-human as well as in natural, companion-like human-machine interactions. Speech conveys content, but also emotions and interaction patterns determining the nature and quality of the user’s relationship to his counterpart. Hence, we consider emotion recognition from speech in the wider sense of application in Companion-systems. This requires a dedicated annotation process to label emotions and to describe their temporal evolution in view of a proper regulation and control of a system’s reaction. This problem is peculiar for naturalistic interactions, where the emotional labels are no longer a priori given. This calls for generating and measuring of a reliable ground truth, where the measurement is closely related to the usage of appropriate emotional features and classification techniques. Further, acted and naturalistic spoken data has to be available in operational form (corpora) for the development of emotion classification; we address the difficulties arising from the variety of these data sources. Speaker clustering and speaker adaptation will as well improve the emotional modeling. Additionally, a combination of the acoustical affective evaluation and the interpretation of non-verbal interaction patterns will lead to a better understanding of and reaction to user-specific emotional behavior.

/pdf/emotion-recognition-from-speech-3q8ss9glh7.pdf

Emotion Recognition from Speech

In emotion recognition from speech, a good transcription and annotation of given material is crucial. Moreover, the question of how to find good emotional labels for new data material is a basic issue. It is not only the question of which emotion labels to choose, it is also a matter of how labellers can cope with annotation methods. In this paper, we present our investigations for emotional labelling with three different methods (Basic Emotions, Geneva Emotion Wheel and Self Assessment Manikins) and compare them in terms of emotion coverage and usability. We show that emotion labels derived from Geneva Emotion Wheel or Self Assessment Manikins fulfill our requirements, but Basic Emotions are not feasible for emotion labelling from spontaneous speech.

Appropriate emotional labelling of non-acted speech using basic emotions, geneva emotion wheel and self assessment manikins

Recently, automatic emotion recognition from speech has achieved growing interest within the human-machine interaction research community. Most part of emotion recognition methods use context independent frame-level analysis or turn-level analysis. In this article, we introduce context dependent vowel level analysis applied for emotion classification. An average first formant value extracted on vowel level has been used as unidimensional acoustic feature vector. The Neyman-Pearson criterion has been used for classification purpose. Our classifier is able to detect high-arousal emotions with small error rates. Within our research we proved that the smallest emotional unit should be the vowel instead of the word. We find out that using vowel level analysis can be an important issue during developing a robust emotion classifier. Also, our research can be useful for developing robust affective speech recognition methods and high quality emotional speech synthesis systems.

Vowels formants analysis allows straightforward detection of high arousal emotions

For successful human–machine-interaction (HCI) the pure textual information and the individual skills, preferences, and affective states of the user must be known. Therefore, as a starting point, the user’s actual affective state has to be recognized. In this work we investigated how additional knowledge, for example age and gender of the user, can be used to improve recognition of affective state. Two methods from automatic speech recognition are used to incorporate age and gender differences in recognition of affective state: speaker group-dependent (SGD) modelling and vocal tract length normalisation (VTLN). The investigations were performed on four corpora with acted and natural affected speech. Different features and two methods of classification (Gaussian mixture models (GMMs) and multi-layer perceptrons (MLPs)) were used. In addition, the effects of channel compensation and contextual characteristics were analysed. The results are compared with our own baseline results and with results reported in the literature. Two hypotheses were tested. First, incorporation of age information further improves speaker group-dependent modelling. Second, acoustic normalization does not achieve the same improvement as achieved by speaker group-dependent modelling, because the age and gender of a speaker affects the way emotions are expressed.

Ingo Siegert

Papers

Inter-rater reliability for emotion annotation in human–computer interaction: comparison and methodological improvements

Emotion Recognition from Speech

Appropriate emotional labelling of non-acted speech using basic emotions, geneva emotion wheel and self assessment manikins

Vowels formants analysis allows straightforward detection of high arousal emotions

Investigation of Speaker Group-Dependent Modelling for Recognition of Affective States from Speech