Wavelet packet analysis for speaker-independent emotion recognition

Wang, Kunxia; Su, Guoxin; Liu, Li; Wang, Shu

doi:10.1016/j.neucom.2020.02.085

Cited by 59 publications

(12 citation statements)

References 30 publications

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“…It is clear from Table 10 that our proposed clustering base GA improve the recognition performance of the SER system when compared to the state of the art systems. [71], [72], which are showing even better scores than ours. However, their parameters in terms of classification algorithms, feature sets, and feature engineering methods are different.…”

Section: Comparison With Existing Work In the Literaturecontrasting

confidence: 49%

Speech Emotion Recognition Using Clustering Based GA-Optimized Feature Set

Kanwal¹,

Asghar

2021

IEEE Access

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Speech Emotion Recognition (SER) is a popular topic in academia and industry. Feature engineering plays a pivotal role in building an efficient SER. Although researchers have done a tremendous amount of work in this field, there are still the issues of speech feature choice and the correct application of feature engineering that remains to be solved in the domain of SER. In this research, a feature optimization approach that uses a clustering-based genetic algorithm is proposed. Instead of randomly selecting the new generation, clustering is applied at the fitness evaluation level to detect outliers for exclusion to be part of the next generation. The approach is compared with the standard Genetic Algorithm in the context of audio emotion recognition using Berlin Emotional Speech Database (EMO-DB), Ryerson Audio-Visual Database of Speech and Song (RAVDESS) and, Surrey Audio-Visual Expressed Emotion Dataset (SAVEE). Results signify that the proposed technique effectively improved the emotion classification in speech. The recognition rate of 89.6% for general speakers (both male and female), 86.2% for male speakers, and 88.3% for female speakers on EMO-DB, 82.5% for general speakers, 75.4% for male speakers, and 91.1% for female speaker on RAVDESS, and 77.7% for general speakers on SAVEE is obtained in speaker-dependent experiments. For speaker-independent experiments, we achieved the recognition rate of 77.5% on EMO-DB, 76.2% on RAVDESS and, 69.8 % on SAVEE. All the experiments were performed on MATLAB and the Support Vector Machine (SVM) was used for classification. Results confirm that the proposed method is capable of discriminating emotions effectively and performed better than the other approaches used for comparison in terms of performance measures.

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Section: Comparison With Existing Work In the Literaturecontrasting

confidence: 49%

Speech Emotion Recognition Using Clustering Based GA-Optimized Feature Set

Kanwal¹,

Asghar

2021

IEEE Access

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“…The most common problem in speech processing is the effect of meddling of noise in the speech signals. The noise masks the speech signal reduces the quality and the speech is greatly affected by presence of backdrop noise [1][2][3][4][5][6][7][8][9][10][11], Noise shrinking or speech enrichment algorithm is to improve the performance of communication systems when their input or output signals are corrupted by noise signal [14].…”

Section: -Speech Signalsmentioning

confidence: 99%

“…Compressed waves are used. When the high-bandwidth waves span, they correspond to the low-frequency signals [14][15][16][17], at lower bands, it corresponds to rapidly changing signals that consist of high frequencies. Unlike other transmission tools (Fourier transforms, etc.)…”

Section: -Wavelet Transformmentioning

confidence: 99%

Denoising and Enhancement Speech Signal Using Wavelet

brahim¹

2021

JIST

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“…The authors of [8] applied wavelet packet analysis to extract emotions from speech signals. The extraction of effective features from speech signals is necessary to recognize different emotions.…”

Section: Semantic Analysis After Extraction Of Publication Informationmentioning

confidence: 99%

Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence

Yusupova

Bogdanova

Komendantova

et al. 2021

Digital

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The topic of affective computing has been growing rapidly in recent times. In the last five years, the volume of publications in this field has tripled. The question arises which research trends are most in demand today. This can only be judged by analysing the publications that present the results of research. Since researchers have access to the entire global scientific publication space, the task of analysing big data arises. This leads to the problem of identifying the most significant results in the subject area of interest. This paper presents some results of the analysis of semi-structured information from scientific citation databases on the subject of “affective computing”.

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Wavelet packet analysis for speaker-independent emotion recognition

Cited by 59 publications

References 30 publications

Speech Emotion Recognition Using Clustering Based GA-Optimized Feature Set

Speech Emotion Recognition Using Clustering Based GA-Optimized Feature Set

Denoising and Enhancement Speech Signal Using Wavelet

Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence

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