How’s my Mood and Stress? An Efficient Speech Analysis Library for Unobtrusive Monitoring on Mobile Phones

Chang, Keh‐Minn; Fisher, D. Grant; Canny, John; Hartmann, Bjoern

doi:10.4108/icst.bodynets.2011.247079

Cited by 41 publications

(37 citation statements)

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“…VibeFones [Madan and Pentland 2006] require a long-term analysis to derive standard deviations of the features and do not describe the process of concluding to the particular feature set. AMMON [Chang et al 2011] showed the performance improvements achieved when combining prosodic features with glottal timings 4 . However, the system was evaluated off-line on datasets created in constrained environments while the performance in real-world situations was not provided.…”

Section: Auditorymentioning

confidence: 99%

“…As shown, existing literature has focussed on inferring stress through auditory, activity and physiological cues. AMMON [Chang et al 2011] was able to manage 84.4% accuracy through prosody including glottal features and utterances given the trade-off of computational burden introduced by eigenvalues solving and other glottal features. In StressSense as expected the personalised classifier achieved the highest accuracy.…”

Section: Stressmentioning

confidence: 99%

“…VibeFones [Madan and Pentland 2006] focussed on pitch, amplitude, mean energy, frequency of the fundamental format and spectral entropy. AMMON [Chang et al 2011] calculated zero crossing rate (ZCR), root mean square, frame energy, pitch, harmonics-to-noise ratio and Mel-Frequency Ceptral Co-efficients (MFCC). StressSense reported that pitch and its derivatives were the most informative features, followed by jitter, spectral centroid, high frequency ratio, speaking rate and MFCCs.…”

Section: Auditorymentioning

confidence: 99%

“…AMMON [Chang et al 2011] extracted prosodic features and utterances which were fed in a linear SVM and performed stress classification with 84.4% accuracy and 93.6% for stress increase-decrease. StressSense ] exploited three different approaches to train two GMMs for stressed and neutral voice.…”

Section: Stressmentioning

confidence: 99%

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A Survey on Mobile Social Signal Processing

et al. 2016

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Understanding human behaviour in an automatic but non-intrusive manner is an important area for various applications. This requires the collaboration of information technology with human sciences to transfer existing knowledge of human behaviour into self-acting tools. These tools will reduce human error that is introduced by current obtrusive methods such as questionnaires. To achieve unobtrusiveness, we focus on exploiting the pervasive and ubiquitous character of mobile devices.In this article, a survey of existing techniques for extracting social behaviour through mobile devices is provided. Initially we expose the terminology used in the area and introduce a concrete architecture for social signal processing applications on mobile phones, constituted by sensing, social interaction detection, behavioural cues extraction, social signal inference and social behaviour understanding. Furthermore, we present state-of-the-art techniques applied to each stage of the process. Finally, potential applications are shown while arguing about the main challenges of the area.

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Section: Auditorymentioning

confidence: 99%

Section: Stressmentioning

confidence: 99%

Section: Auditorymentioning

confidence: 99%

Section: Stressmentioning

confidence: 99%

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A Survey on Mobile Social Signal Processing

et al. 2016

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“…There has been a growing interest in inferring stress and emotions from recording speech using mobile phone-based sensors. The Affective and Mental health MONitor (AMMON) [40,41] is a speech analysis library that is designed to work on mobile phones, to recognize emotions and analyze the mental stress of the user based on voice. AMMON is evaluated against speech under simulated and actual stress (SUSAS) [42], a dataset which is the most common dataset used for stress detection tasks.…”

Section: Speech-based Analysismentioning

confidence: 99%

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Kanjo

Al-Husain

Chamberlain

2015

Pers Ubiquit Comput

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Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely ''sensing,'' ''analysis,'' and ''application.'' Sensing investigates the different sensing modalities that are used in existing real-time affective applications, analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing.

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