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

Data Mining Practical Machine Learning Tools and Techniques

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

Model selection strategies for machine learning algorithms typically involve the numerical optimisation of an appropriate model selection criterion, often based on an estimator of generalisation performance, such as k-fold cross-validation. The error of such an estimator can be broken down into bias and variance components. While unbiasedness is often cited as a beneficial quality of a model selection criterion, we demonstrate that a low variance is at least as important, as a non-negligible variance introduces the potential for over-fitting in model selection as well as in training the model. While this observation is in hindsight perhaps rather obvious, the degradation in performance due to over-fitting the model selection criterion can be surprisingly large, an observation that appears to have received little attention in the machine learning literature to date. In this paper, we show that the effects of this form of over-fitting are often of comparable magnitude to differences in performance between learning algorithms, and thus cannot be ignored in empirical evaluation. Furthermore, we show that some common performance evaluation practices are susceptible to a form of selection bias as a result of this form of over-fitting and hence are unreliable. We discuss methods to avoid over-fitting in model selection and subsequent selection bias in performance evaluation, which we hope will be incorporated into best practice. While this study concentrates on cross-validation based model selection, the findings are quite general and apply to any model selection practice involving the optimisation of a model selection criterion evaluated over a finite sample of data, including maximisation of the Bayesian evidence and optimisation of performance bounds.

/pdf/on-over-fitting-in-model-selection-and-subsequent-selection-3to4mouyqd.pdf

On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

Work on voice sciences over recent decades has led to a proliferation of acoustic parameters that are used quite selectively and are not always extracted in a similar fashion. With many independent teams working in different research areas, shared standards become an essential safeguard to ensure compliance with state-of-the-art methods allowing appropriate comparison of results across studies and potential integration and combination of extraction and recognition systems. In this paper we propose a basic standard acoustic parameter set for various areas of automatic voice analysis, such as paralinguistic or clinical speech analysis. In contrast to a large brute-force parameter set, we present a minimalistic set of voice parameters here. These were selected based on a) their potential to index affective physiological changes in voice production, b) their proven value in former studies as well as their automatic extractability, and c) their theoretical significance. The set is intended to provide a common baseline for evaluation of future research and eliminate differences caused by varying parameter sets or even different implementations of the same parameters. Our implementation is publicly available with the openSMILE toolkit. Comparative evaluations of the proposed feature set and large baseline feature sets of INTERSPEECH challenges show a high performance of the proposed set in relation to its size.

/pdf/the-geneva-minimalistic-acoustic-parameter-set-gemaps-for-36spb4mksx.pdf

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

As the recognition of emotion from speech has matured to a degree where it becomes applicable in real-life settings, it is time for a realistic view on obtainable performances. Most studies tend to overestimation in this respect: Acted data is often used rather than spontaneous data, results are reported on preselected prototypical data, and true speaker disjunctive partitioning is still less common than simple cross-validation. Even speaker disjunctive evaluation can give only a little insight into the generalization ability of today's emotion recognition engines since training and test data used for system development usually tend to be similar as far as recording conditions, noise overlay, language, and types of emotions are concerned. A considerably more realistic impression can be gathered by interset evaluation: We therefore show results employing six standard databases in a cross-corpora evaluation experiment which could also be helpful for learning about chances to add resources for training and overcoming the typical sparseness in the field. To better cope with the observed high variances, different types of normalization are investigated. 1.8 k individual evaluations in total indicate the crucial performance inferiority of inter to intracorpus testing.

Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies

In the light of the first challenge on emotion recognition from speech we provide the largest-to-date benchmark comparison under equal conditions on nine standard corpora in the field using the two pre-dominant paradigms: modeling on a frame-level by means of Hidden Markov Models and supra-segmental modeling by systematic feature brute-forcing. Investigated corpora are the ABC, AVIC, DES, EMO-DB, eNTERFACE, SAL, SmartKom, SUSAS, and VAM databases. To provide better comparability among sets, we additionally cluster each database's emotions into binary valence and arousal discrimination tasks. In the result large differences are found among corpora that mostly stem from naturalistic emotions and spontaneous speech vs. more prototypical events. Further, supra-segmental modeling proves significantly beneficial on average when several classes are addressed at a time.

Acoustic emotion recognition: A benchmark comparison of performances

Opposing the pre-dominant turn-wise statistics of acoustic Low-Level-Descriptors followed by static classification we re-investigate dynamic modeling directly on the frame-level in speech-based emotion recognition. This seems beneficial, as it is well known that important information on temporal sub-turn-layers exists. And, most promisingly, we integrate this frame-level information within a state-of-the-art large-feature-space emotion recognition engine. In order to investigate frame-level processing we employ a typical speaker-recognition set-up tailored for the use of emotion classification. That is a GMM for classification and MFCC plus speed and acceleration coefficients as features. We thereby also consider use of multiple states, respectively an HMM. In order to fuse this information with turn-based modeling, output scores are added to a super-vector combined with static acoustic features. Thereby a variety of Low-Level-Descriptors and functionals to cover prosodic, speech quality, and articulatory aspects are considered. Starting from 1.4k features we select optimal configurations including and excluding GMM information. The final decision task is realized by use of SVM. Extensive test-runs are carried out on two popular public databases, namely EMO-DB and SUSAS, to investigate acted and spontaneous data. As we face the current challenge of speaker-independent analysis we also discuss benefits arising from speaker normalization. The results obtained clearly emphasize the superior power of integrated diverse time-levels.

Frame vs. Turn-Level: Emotion Recognition from Speech Considering Static and Dynamic Processing

The Transregional Collaborative Research Centre SFB/TRR 62 "A Companion Technology for Cognitive Technical Systems", funded by the German Research Foundation (DFG) at Ulm and Magdeburg sites, deals with the systematic and interdisciplinary study of cognitive abilities and their implementation in technical systems. The properties of multimodality, individuality, adaptability, availability, cooperativeness and trustworthiness are at the focus of the investigation. These characteristics show a new type of interactive device which is not only practical and efficient to operate, but as well agreeable, hence the term "companion". The realisation of such a technology is supported by technical advancement as well as by neurobiological findings. Companion technology has to consider the entire situation of the user, machine, environment and (if applicable) other people or third interacting parties, in current and historical states. This will reflect the mental state of the user, his embeddedness in the task, and how he is situated in the current process.

A companion technology for cognitive technical systems

Current approaches to the recognition of emotion within speech usually use statistic feature information obtained by application of functionals on turnor chunk levels. Yet, it is well known that thereby important information on temporal sub-layers as the frame-level is lost. We therefore investigate the benefits of integration of such information within turn-level feature space. For frame-level analysis we use GMM for classification and 39 MFCC and energy features with CMS. In a subsequent step output scores are fed forward into a 1.4k large-feature-space turn-level SVM emotion recognition engine. Thereby we use a variety of LowLevel-Descriptors and functionals to cover prosodic, speech quality, and articulatory aspects. Extensive testruns are carried out on the public databases EMO-DB and SUSAS. Speaker-independent analysis is faced by speaker normalization. Overall results highly emphasize the benefits of feature integration on diverse time scales.

https://mediatum.ub.tum.de/doc/1138568/file.pdf

Andreas Wendemuth

Papers

Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies

Acoustic emotion recognition: A benchmark comparison of performances

Frame vs. Turn-Level: Emotion Recognition from Speech Considering Static and Dynamic Processing

A companion technology for cognitive technical systems

Combining Frame and Turn-Level Information for Robust Recognition of Emotions within Speech