Affective encoding in the speech signal and in event-related brain potentials

A number of perceptual features have been utilized for the characterization of the emotional state of a speaker. However, for automatic recognition suitable objective features are needed. We have examined several features of the speech signal in relation to accentuation and traces of event-related brain potentials (ERPs) during affective speech perception. Concerning the features of the speech signal we focus on measures related to breathiness and roughness. The objective measures used were an estimation of the harmonics-to-noise ratio, the glottal-to-noise excitation ratio, a measure for spectral flatness, as well as the maximum prediction gain for a speech production model computed by the mutual information function and the ERPs. Results indicate that in particular the maximum prediction gain shows a good differentiation between neutral and non-neutral emotional speaker state. This differentiation is partly comparable to the ERP results that show a differentiation of neutral, positive and negative affect. Other objective measures are more related to accentuation than to emotional state of the speaker

Empathic Agent Technology (EAT)

A new view on empathic agents is introduced, named: Empathic Agent Technology (EAT). It incorporates a speech analysis, which provides an indication for the amount of tension present in people. It is founded on an indirect physiological measure for the amount of experienced stress, defined as the variability of the fundamental frequency of the human voice. A thorough review of literature is provided on which the EAT is founded. In addition, the complete processing line of this measure is introduced. Hence, the first generally applicable, completely automated technique is introduced that enables the development of truly empathic agents

Cross validation of bi-modal health-related stress assessment

This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care

Towards an artificial therapy assistant: Measuring excessive stress from speech

The measurement of (excessive) stress is still a challenging endeavor. Most tools rely on either introspection or expert opinion and are, therefore, often less reliable or a burden on the patient. An objective method could relieve these problems and, consequently, assist diagnostics. Speech was considered an excellent candidate for an objective, unobtrusive measure of emotion. True stress was successfully induced, using two storytelling\ud sessions performed by 25 patients suffering from a stress disorder. When reading either a happy or a sad story, different stress levels were reported using the Subjective Unit of Distress (SUD). A linear regression model consisting of the high-frequency energy, pitch, and zero crossings of the speech signal was able to explain 70% of the variance in the subjectively reported stress. The results demonstrate the feasibility of an objective measurement of stress in speech. As such, the foundation for an Artificial Therapeutic Agent is laid, capable of assisting therapists through an objective measurement of experienced stress

Ubiquitous emotion-aware computing

Emotions are a crucial element for personal and ubiquitous computing. What to sense and how to sense it, however, remain a challenge. This study explores the rare combination of speech, electrocardiogram, and a revised Self-Assessment Mannequin to assess people’s emotions. 40 people watched 30 International Affective Picture System pictures in either an office or a living-room environment. Additionally, their personality traits neuroticism and extroversion and demographic information (i.e., gender, nationality, and level of education) were recorded. The resulting data were analyzed using both basic emotion categories and the valence--arousal model, which enabled a comparison between both representations. The combination of heart rate variability and three speech measures (i.e., variability of the fundamental frequency of pitch (F0), intensity, and energy) explained 90% (p < .001) of the participants’ experienced valence--arousal, with 88% for valence and 99% for arousal (ps < .001). The six basic emotions could also be discriminated (p < .001), although the explained variance was much lower: 18–20%. Environment (or context), the personality trait neuroticism, and gender proved to be useful when a nuanced assessment of people’s emotions was needed. Taken together, this study provides a significant leap toward robust, generic, and ubiquitous emotion-aware computing

Emotional Prosody Measurement (EPM): A voice-based evaluation method for psychological therapy effectiveness

The voice embodies three sources of information: speech, the identity, and the emotional state of the speaker (i.e., emotional prosody). The latter feature is resembled by the variability of the F0 (also named fundamental frequency of pitch) (SD F0). To extract this feature, Emotional Prosody Measurement (EPM) was developed, which consists of 1) speech recording, 2) removal of speckle noise, 3) a Fourier Transform to extract the F0-signal, and 4) the determination of SD F0. After a pilot study in which six participants mimicked emotions by their voice, the core experiment was conducted to see whether EPM is successful. Twenty-five patients suffering from a panic disorder with agoraphobia participated. Two methods (storytelling and reliving) were used to trigger anxiety and were compared with comparable but more relaxed conditions. This resulted in a unique database of speech samples that was used to compare the EPM with the Subjective Unit of Distress to validate it as measure for anxiety/stress. The experimental manipulation of anxiety proved to be successful and EPM proved to be a successful evaluation method for psychological therapy effectiveness

Biometrics for Emotion Detection (BED): Exploring the combination of Speech and ECG

The paradigm Biometrics for Emotion Detection (BED) is introduced, which enables unobtrusive emotion recognition, taking into account varying environments. It uses the electrocardiogram (ECG) and speech, as a powerful but rarely used combination to unravel people’s emotions. BED was applied in two environments (i.e., office and home-like) in which 40 people watched 6 film scenes. It is shown that both heart rate variability (derived from the ECG) and, when people’s gender is taken into account, the standard deviation of the fundamental frequency of speech indicate people’s experienced emotions. As such, these measures validate each other. Moreover, it is found that people’s environment can indeed of influence experienced emotions. These results indicate that BED might become an important paradigm for unobtrusive emotion detection