A Study of Speech Emotion Recognition and Its Application to Mobile Services

Yoon, Won-Joong; Cho, Youn-Ho; Park, Kyu-Sik

doi:10.1007/978-3-540-73549-6_74

Cited by 29 publications

(12 citation statements)

References 7 publications

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“…Although many combinations of emotional features and classifiers have been presented and evaluated in the literature especially in the context of Human Computer Interaction, little attention has been paid to speech emotion recognition using mobile technology. In [19] for instance, authors propose a speech emotion recognition agent for mobile communication service. They argue that the agent is capable of determining the degree of affection (love, truthfulness, weariness, trick, friendship) of a person, in real-time conversation through a cellular phone, at an accuracy of 72.5 % over five predetermined emotional states (neutral, happiness, sadness, anger, and annoyance).…”

Section: Emotion Recognition In Speechmentioning

confidence: 99%

“…In [20] and [21] emotion recognition is processed locally within the mobile phone, whereas in [19], the speech signal is transmitted to an emotion recognition server which performs all the computation and processing and reports back a classification result to the mobile agent based on the confidence probability of each emotional state.…”

Section: Emotion Recognition In Speechmentioning

confidence: 99%

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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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Section: Emotion Recognition In Speechmentioning

confidence: 99%

Section: Emotion Recognition In Speechmentioning

confidence: 99%

The Human Face of Mobile

Mousannif

Khalil

2014

Information and Communication Technology

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“…On the contrary our proposed system aims at recognizing the users' emotions through their interaction with a mobile device rather than generating emotions. As a second related approach we found that Yoon et al [11] propose a speech emotion recognition agent for mobile communication service. This system tries to recognize five emotional states, namely neutral emotional state, happiness, sadness, anger, and annoyance from the speech captured by a cellular phone in real time and then it calculates the degree of affection such as love, truthfulness, weariness, trick, and friendship.…”

Section: Affective Interaction In Mobile Devicesmentioning

confidence: 99%

Mobile Education: Towards Affective Bi-modal Interaction for Adaptivity

Alepis¹,

Virvou²,

Kabassi³

2009

Int. J. Interact. Mob. Technol.

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Abstract-One important field where mobile technology can make significant contributions is education. However one criticism in mobile education is that students receive impersonal teaching. Affective computing may give a solution to this problem. In this paper we describe an affective bi-modal educational system for mobile devices. In our research we describe a novel approach of combining information from two modalities namely the keyboard and the microphone through a multi-criteria decision making theory.

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“…While noise-robust automatic speech recognition (ASR) has been an active field of research for years, with a considerable amount of wellelaborated techniques available [1], few studies so far dealt with the challenge of noise-robust AER, such as [2,3]. Besides, at present the tools and particularly evaluation methodologies for noise-robust AER are rather basic: often, they are constrained to elementary feature enhancement and selection techniques [4,5], are characterized by the simplification of additive stationary noise [6,7], or are limited to matched condition training [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Nonprototypical Emotions in Reverberated and Noisy Speech by Nonnegative Matrix Factorization

Weninger

Schuller

Batliner

et al. 2011

EURASIP J. Adv. Signal Process.

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We present a comprehensive study on the effect of reverberation and background noise on the recognition of nonprototypical emotions from speech. We carry out our evaluation on a single, well-defined task based on the FAU Aibo Emotion Corpus consisting of spontaneous children's speech, which was used in the INTERSPEECH 2009 Emotion Challenge, the first of its kind. Based on the challenge task, and relying on well-proven methodologies from the speech recognition domain, we derive test scenarios with realistic noise and reverberation conditions, including matched as well as mismatched condition training. As feature extraction based on supervised Nonnegative Matrix Factorization (NMF) has been proposed in automatic speech recognition for enhanced robustness, we introduce and evaluate different kinds of NMF-based features for emotion recognition. We conclude that NMF features can significantly contribute to the robustness of state-of-the-art emotion recognition engines in practical application scenarios where different noise and reverberation conditions have to be faced.

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A Study of Speech Emotion Recognition and Its Application to Mobile Services

Cited by 29 publications

References 7 publications

The Human Face of Mobile

The Human Face of Mobile

Mobile Education: Towards Affective Bi-modal Interaction for Adaptivity

Recognition of Nonprototypical Emotions in Reverberated and Noisy Speech by Nonnegative Matrix Factorization

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