Robust Feature Extraction for Mobile-Based Speech Emotion Recognition System

Lee, Kang-Kue; Cho, Youn-Ho; Park, Kyu-Sik

doi:10.1007/11816515_48

Cited by 3 publications

(5 citation statements)

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“…Several codecs have been developed to meet various applications with different quality requirements (Siegert et al, 2016a). More details about the degradation of acoustic characteristics under compressed speech can be found in (Byrne and Foulkes, 2004;Lee et al, 2006;Siegert et al, 2016b).…”

Section: Audio Codecsmentioning

confidence: 99%

Case Report: Women, Be Aware that Your Vocal Charisma can Dwindle in Remote Meetings

Siegert

Niebuhr

2021

Front. Commun.

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Remote meetings via Zoom, Skype, or Teams limit the range and richness of nonverbal communication signals. Not just because of the typically sub-optimal light, posture, and gaze conditions, but also because of the reduced speaker visibility. Consequently, the speaker’s voice becomes immensely important, especially when it comes to being persuasive and conveying charismatic attributes. However, to offer a reliable service and limit the transmission bandwidth, remote meeting tools heavily rely on signal compression. It has never been analyzed how this compression affects a speaker’s persuasive and overall charismatic impact. Our study addresses this gap for the audio signal. A perception experiment was carried out in which listeners rated short stimulus utterances with systematically varied compression rates and techniques. The scalar ratings concerned a set of charismatic speaker attributes. Results show that the applied audio compression significantly influences the assessment of a speaker’s charismatic impact and that, particularly female speakers seem to be systematically disadvantaged by audio compression rates and techniques. Their charismatic impact decreases over a larger range of different codecs; and this decrease is additionally also more strongly pronounced than for male speakers. We discuss these findings with respect to two possible explanations. The first explanation is signal-based: audio compression codecs could be generally optimized for male speech and, thus, degrade female speech more (particularly in terms of charisma-associated features). Alternatively, the explanation is in the ears of the listeners who are less forgiving of signal degradation when rating female speakers’ charisma.

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Section: Audio Codecsmentioning

confidence: 99%

Case Report: Women, Be Aware that Your Vocal Charisma can Dwindle in Remote Meetings

Siegert

Niebuhr

2021

Front. Commun.

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“…Therefore, there are several types of data sources such as speech, text, facial expression, body movement, and physiological measurement like EEG, finger temperature, skin conductance level, heart rate, and muscle activity [11,13,14,69]–[73]. There is a mass of emotion data for speech, text, and facial expression as these are details that can be easily gathered from devices used by people on a daily basis, such as cellphones and computers [75,76]. On the other hand, physiological signals can also be a good source of information since they could be collected continuously without participants interfering [77,78].…”

Section: Eegmentioning

confidence: 99%

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks

Movahedi

Coyle

Sejdić

2018

IEEE J. Biomed. Health Inform.

103

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Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this paper, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state-of-the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.

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“…For minimization of (4), we implemented the algorithm by Lee and Seung [31], which iteratively modifies W and H using "multiplicative update" rules. With matrix-matrix multiplication being its core operation, the computational cost of this algorithm largely depends on the matrix dimensions: assuming a naive implementation of matrixmatrix multiplication, the cost per iteration step is O(mnr) for the minimization of c d from (4). However, in practice, computation time can be drastically reduced by using optimized linear algebra routines.…”

Section: Factorization Algorithms a Factorization According Tomentioning

confidence: 99%

“…Table 3); "+" denotes the union of feature sets. "Mel" are functionals of 26 Mel frequency bands and "MFCC" functionals of the corresponding MFCCs (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Classification was performed by SVM (trained with SMO, complexity C = 0.1).…”

Section: Interspeech 2009 Emotion Challenge Taskmentioning

confidence: 99%

“…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%

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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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Robust Feature Extraction for Mobile-Based Speech Emotion Recognition System

Cited by 3 publications

References 0 publications

Case Report: Women, Be Aware that Your Vocal Charisma can Dwindle in Remote Meetings

Case Report: Women, Be Aware that Your Vocal Charisma can Dwindle in Remote Meetings

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks

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

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