A new approach of audio emotion recognition

Ooi, Chien Shing; Seng, Kah Phooi; Ang, Li-Minn; Chew, Li Wern

doi:10.1016/j.eswa.2014.03.026

Cited by 100 publications

(37 citation statements)

References 45 publications

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“…A C C E P T E D M A N U S C R I P T Yelure, & Tayade, 2015;Julia & Iftekharuddin, 2005;Zhao et al, 2014); or in six emotions: happy, anger, fear, sadness, surprise and disgust (Balti & Elmaghraby, 2014;Kanagaraj, Shahina, Devosh, & Kamalakannan, 2014;Ooi, Seng, Ang, & Chew, 2014;Rabiei & Gasparetto, 2014;Razak, Komiya, Izani, & Abidin, 2005). Some authors extend the list distinguishing between cold and hot anger (Yacoub et al, 2003), or adding other emotions such as boredom (Le & Lee, 2014;Tawari & Trivedi, 2010;Xiao, Dellandrea, Dou, & Chen, 2007), pride, panic (V. Petrushin, 1999) Nevertheless, other researches, focused on the development of real-time applications and on the detection of changes in the speaker's affective state, emphasize the usefulness of represent emotions in an evaluation plane in terms of two or more levels or continuous dimensions (Laukka, 2004;K.…”

Section: Accepted Manuscriptmentioning

confidence: 97%

New approach in quantification of emotional intensity from the speech signal: emotional temperature

Alonso¹,

Cabrera²,

Medina³

et al. 2015

Expert Systems with Applications

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Section: Accepted Manuscriptmentioning

confidence: 97%

New approach in quantification of emotional intensity from the speech signal: emotional temperature

Alonso¹,

Cabrera²,

Medina³

et al. 2015

Expert Systems with Applications

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“…In [7] proposed new architecture of intelligent audio emotion recognition. It fully utilizes both prosodic and spectral features in its design.…”

Section: Literature Surveymentioning

confidence: 99%

Recognition and Classification of Human Emotion from Audio

Pawar¹

2017

IJARCSSE

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Abstract-In this paper, the audio emotion recognition system is proposed that uses a mixture of rule-based and machine learning techniques to improve the recognition efficacy in the audio paths. The audio path is designed using a combination of input prosodic features (pitch, log-energy, zero crossing rates and Teager energy operator) and spectral features (Mel-scale frequency cepstral coefficients). Mel-Frequency Cepstral Coefficients (MFCC) feature extraction method is a leading approach for speech feature extraction and current research aims to identify performance enhancements. After the MFCC feature extraction, these features are passed to three parallel sub-paths which use feature extraction and classification techniques (i.e. BDPCA+LSLDA+RBF). In addition, Naïve Bays and SVM classifier are presented with BDPCS and LSLDA for evaluation of emotion. The extracted audio features are passed into an audio feature level fusion module that uses a set of rules to determine the most likely emotion contained in the audio signal. The performances of the proposed audio path and the final system are evaluated on standard databases of audio clips extracted from the video.Keywords-Emotion recognition, audio-visual processing, rule-based, machine learning, multimodal system I. INTRODUCTIONEmotion recognition is an automated process to identify the affective state of a person and has gained the increasing attention of researchers in the human-computer interaction (HCI) field for various applications like automotive safety, gaming experiences, mental diagnosis in military service, customer services, etc. Over the decades, several research efforts have been conducted for audio-visual emotion recognition. In the literature, three main approaches can be broadly distinguished: (i) audio based approaches, (ii) visual-based approaches, and (iii) audio-visual approaches. Initial works focused on treating the audio data and visual data modalities separately. The audio-based emotion recognition efforts are based on extracting and recognizing the emotional states contained in the human speech signal. An important issue is the selection of the salient features to be used for discriminating the different emotions. Two types of features have been found to be useful for recognizing emotion in speech: prosodic and spectral features. Examples of commonly used prosodic features are pitch and energy and examples of commonly used spectral features are Mel-scale frequency cepstral coefficients (MFCC). Although prosodic features are commonly used in many works some researchers have demonstrated the usefulness of spectral features for speech emotion recognition. The monograph work further investigated combining different types of features like prosodic and spectral features for audio-based emotion recognition. The visual-based emotion recognition efforts are based on extracting and recognizing the emotional states contained in the human facial expression. An example is a recent work by Tawari & Trivedi which used a representation of image sequen...

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“…Examples of acoustic classification problems apart from vowel recognition that satisfy the requirements for the applicability of the fuzzy FITA include automatic emotion classification (Ooi et al, 2014), animal vocalization classification (Clemins et al, 2005;Chen and Maher, 2006;Binder and Hines, 2014), aircraft classification (S anchez Fern andez et al, 2013), and musical genre classification (Tzanetakis and Cook, 2002). Where some of these studies do not exactly satisfy all the applicability requirements, they can be adjusted to fit the requirements by adding background noise or increasing the number of classes.…”

Section: B Applicability Of the Fuzzy Fitamentioning

confidence: 99%

Fuzzy information transmission analysis for continuous speech features

Oosthuizen

Hanekom

2015

The Journal of the Acoustical Society of America

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Feature information transmission analysis (FITA) estimates information transmitted by an acoustic feature by assigning tokens to categories according to the feature under investigation and comparing within-category to between-category confusions. FITA was initially developed for categorical features (e.g., voicing) for which the category assignments arise from the feature definition. When used with continuous features (e.g., formants), it may happen that pairs of tokens in different categories are more similar than pairs of tokens in the same category. The estimated transmitted information may be sensitive to category boundary location and the selected number of categories. This paper proposes a fuzzy approach to FITA that provides a smoother transition between categories and compares its sensitivity to grouping parameters with that of the traditional approach. The fuzzy FITA was found to be sufficiently robust to boundary location to allow automation of category boundary selection. Traditional and fuzzy FITA were found to be sensitive to the number of categories. This is inherent to the mechanism of isolating a feature by dividing tokens into categories, so that transmitted information values calculated using different numbers of categories should not be compared. Four categories are recommended for continuous features when twelve tokens are used.

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A new approach of audio emotion recognition

Cited by 100 publications

References 45 publications

New approach in quantification of emotional intensity from the speech signal: emotional temperature

New approach in quantification of emotional intensity from the speech signal: emotional temperature

Recognition and Classification of Human Emotion from Audio

Fuzzy information transmission analysis for continuous speech features

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