Real Life Emotion Classification using Spectral Features and Gaussian Mixture Models

Koolagudi, Shashidhar G.; Barthwal, Anurag; Devliyal, Swati; Rao, K. Sreenivasa

doi:10.1016/j.proeng.2012.06.447

Cited by 6 publications

(1 citation statement)

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“…This design depends on many aspects such as finding the best machine learning algorithm (neural networks, support vector machines, etc.) to use in constructing the classifier [14], the suitable architecture for the classifier [15], [16], or the proper technique to use when extracting features [17]. The last aspect is the proper preparation of an emotional speech database for evaluating system performance [18].…”

Section: Emotion Recognition In Speechmentioning

confidence: 99%

The Human Face of Mobile

Mousannif

Khalil

2014

Information and Communication Technology

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Part 1: Information & Communication Technology-EurAsia Conference 2014, ICT-EurAsia 2014International audienceAs the landscape around Big data continues to exponentially evolve, the « big » facet of Big data is no more number one priority of researchers and IT professionals. The race has recently become more about how to sift through torrents of data to find the hidden diamond and engineer a better, smarter and healthier world. The ease with which our mobile captures daily data about ourselves makes it an exceptionally suitable means for ultimately improving the quality of our lives and gaining valuable insights into our affective, mental and physical state. This paper takes the first exploratory step into this direction by using the mobile to process and analyze the “digital exhaust” it collects to automatically recognize our emotional states and accordingly respond to them in the most effective and “human” way possible. To achieve this we treat all technical, psycho-somatic, and cognitive aspects of emotion observation and prediction, and repackage all these elements into a mobile multimodal emotion recognition system that can be used on any mobile device

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