Speech Emotion Analysis: Exploring the Role of Context

Ashish Tawari · Mohan Manubhai Trivedi

110 citations0/0 files storedIEEE Transactions on Multimedia

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

Automated analysis of human affective behavior has attracted increasing attention in recent years. With the research shift toward spontaneous behavior, many challenges have come to surface ranging from database collection strategies to the use of new feature sets (e.g., lexical cues apart from prosodic features). Use of contextual information, however, is rarely addressed in the field of affect expression recognition, yet it is evident that affect recognition by human is largely influenced by the context information. Our contribution in this paper is threefold. First, we introduce a novel set of features based on cepstrum analysis of pitch and intensity contours. We evaluate the usefulness of these features on two different databases: Berlin Database of emotional speech (EMO-DB) and locally collected audiovisual database in car settings (CVRRCar-AVDB). The overall recognition accuracy achieved for seven emotions in the EMO-DB database is over 84% and over 87% for three emotion classes in CVRRCar-AVDB. This is based on tenfold stratified cross validation. Second, we introduce the collection of a new audiovisual database in an automobile setting (CVRRCar-AVDB). In this current study, we only use the audio channel of the database. Third, we systematically analyze the effects of different contexts on two different databases. We present context analysis of subject and text based on speaker/text-dependent/-independent analysis on EMO-DB. Furthermore, we perform context analysis based on gender information on EMO-DB and CVRRCar-AVDB. The results based on these analyses are promising.

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References

2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2005A database of German emotional speech 1996Acoustic profiles in vocal emotion expression.2002Prosody-based automatic detection of annoyance and frustration in human-computer dialog 1987Mother-infant face-to-face interaction: The sequence of dyadic states at 3, 6, and 9 months.2003Emotion recognition by speech signals 1988Mother-infant face-to-face interaction: Influence is bidirectional and unrelated to periodic cycles in either partner's behavior.2005Challenges in real-life emotion annotation and machine learning based detection

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2022Multi-modal emotion recognition using EEG and speech signals 2011Ubiquitous emotion-aware computing 2020Egyptian Arabic speech emotion recognition using prosodic, spectral and wavelet features 2018Building Emotional Machines: Recognizing Image Emotions Through Deep Neural Networks 2023Speech emotion recognition approaches: A systematic review

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