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

Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

The World is Flat: A Brief History of the Twenty-First Century

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.

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

Hand and Mind: What Gestures Reveal about Thought. David McNeill. Chicago and London: University of Chicago Press, 1992. 416 pp.

Hand and Mind: What Gestures Reveal about Thought

The motivation for this work is in an attempt to rectify the current lack of objective tools for clinical analysis of emotional disorders. This study involves the examination of a large breadth of objectively measurable features for use in discriminating depressed speech. Analysis is based on features related to prosodics, the vocal tract, and parameters extracted directly from the glottal waveform. Discrimination of the depressed speech was based on a feature selection strategy utilizing the following combinations of feature domains: prosodic measures alone, prosodic and vocal tract measures, prosodic and glottal measures, and all three domains. The combination of glottal and prosodic features produced better discrimination overall than the combination of prosodic and vocal tract features. Analysis of discriminating feature sets used in the study reflect a clear indication that glottal descriptors are vital components of vocal affect analysis.

Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech

The threat of obesity, diabetes, anorexia, and bulimia in our society today has motivated extensive research on dietary monitoring. Standard self-report methods such as 24-h recall and food frequency questionnaires are expensive, burdensome, and unreliable to handle the growing health crisis. Long-term activity monitoring in daily living is a promising approach to provide individuals with quantitative feedback that can encourage healthier habits. Although several studies have attempted automating dietary monitoring using wearable, handheld, smart-object, and environmental systems, it remains an open research problem. This paper aims to provide a comprehensive review of wearable and hand-held approaches from 2004 to 2016. Emphasis is placed on sensor types used, signal analysis and machine learning methods, as well as a benchmark of state-of-the art work in this field. Key issues, challenges, and gaps are highlighted to motivate future work toward development of effective, reliable, and robust dietary monitoring systems.

Unobtrusive and Wearable Systems for Automatic Dietary Monitoring

Human communication is saturated with emotional context that aids in interpreting a speakers mental state Speech analysis research involving the classification of emotional states has been studied primarily with prosodic (eg, pitch, energy, speaking rate) and/or spectral (eg, formants) features Glottal waveform features, while receiving less attention (due primarily to the difficulty of feature extraction), have also shown strong clustering potential of various emotional and stress states This study provides a comparison of the major categories of speech analysis in the application of identifying and clustering feature statistics from a control group and a patient group suffering from a clinical diagnosis of depression

Comparing objective feature statistics of speech for classifying clinical depression

Understanding how someone is speaking can be equally important to what they are saying when evaluating emotional disorders, such as depression. In this study, we use the acoustic speech signal to analyze variations in prosodic feature statistics for subjects suffering from a depressive disorder. A new sample database of subjects with and without a depressive disorder is collected and pitch, energy, and speaking rate feature statistics are generated at a sentence level and grouped into a series of observations (subset of sentences) for analysis. A common technique in quantifying an observation had been to simply use the average of the feature statistic for the subset of sentences within an observation. However, we investigate the merit of a series of statistical measures as a means of quantifying a subset of feature statistics to capture emotional variations from sentence to sentence within a single observation. Comparisons with the exclusive use of the average show an improvement in overall separation accuracy for other quantifying statistics.

Analysis of prosodic variation in speech for clinical depression

Classifying emotion and emotion related disorders in the voice have often been studied utilizing prosodic (pitch, energy, speaking rate) and other spectral characteristics (formants, power spectral density) of the acoustic speech signal. Glottal waveform features have received little attention in the study of many emotion and emotion related disorders, but have shown strong correlations in a variety of speech pattern studies including speaker characterization and stress analysis. We employ glottal extraction techniques to obtain features related to timing, ratios, shimmer, and spectral characteristics of the glottal waveform in the study of clinical depression. Our study reports on several glottal waveform features that show very good separation among a control group and patient group of males and females suffering from a depressive disorder.

Elliot Moore

Papers

Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech

Unobtrusive and Wearable Systems for Automatic Dietary Monitoring

Comparing objective feature statistics of speech for classifying clinical depression

Analysis of prosodic variation in speech for clinical depression

Investigating the role of glottal features in classifying clinical depression