Improvement of Thai speech emotion recognition by using face feature analysis

Stanković, Igor; Karnjanadecha, Montri; Delić, Vlado

doi:10.1109/ispacs.2011.6146092

Cited by 11 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Linear kernel: (14) Polynomial kernel: (15) Radial Basis Function (RBF) kernel: (16) A single SVM itself is a classification method for two category data. In speech emotion recognition, there are usually multiple emotion categories.…”

Section: Support Vector Machines (Svm ) For Emotionmentioning

confidence: 99%

“…However, not all of them were useful, meaning that only words that were correctly classified by human ear were used. If an emotion from curtain file was correctly classified by at least 4 out of 5 people, the file was kept, otherwise, it was deleted [15].…”

Section: E Speech Emotion Database 1) Berlin Database Of Emotional Smentioning

confidence: 99%

“…In work [14] lpcc, mfcc were used and reported the classification accuracy of 83.9%. In some other works like [15], energy, zero crossing rate and fundamental frequency were used and reached classification accuracy of 97.8%. In emotion classification, many researchers explored several promising classification methods, such as K-nearest neighbor (k-NN) [16], support vector machines (SVM) [17], Neural network (NN) [18], Hidden Markov model (HMM) [19], etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Speech emotion recognition using Support Vector Machines

Seehapoch¹,

Wongthanavasu

2013

2013 5th International Conference on Knowledge and Smart Technology (KST)

View full text Add to dashboard Cite

Automatic recognition of emotional states from human speech is a current research topic with a wide range. In this paper an attempt has been made to recognize and classify the speech emotion from three language databases, namely, Berlin, Japan and Thai emotion databases. Speech features consisting of Fundamental Frequency (F0), Energy, Zero Crossing Rate (ZCR), Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficient (MFCC) from short-time wavelet signals are comprehensively investigated. In this regard, Support Vector Machines (SVM) is utilized as the classification model. Empirical experimentation shows that the combined features of F0, Energy and MFCC provide the highest accuracy on all databases provided using the linear kernel. It gives 89.80%, 93.57% and 98.00% classification accuracy for Berlin, Japan and Thai emotions databases, respectively.

show abstract

Section: Support Vector Machines (Svm ) For Emotionmentioning

confidence: 99%

Section: E Speech Emotion Database 1) Berlin Database Of Emotional Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Speech emotion recognition using Support Vector Machines

Seehapoch¹,

Wongthanavasu

2013

2013 5th International Conference on Knowledge and Smart Technology (KST)

View full text Add to dashboard Cite

show abstract

“…MFCCs can be calculated according to the instructions given in [57]. MFCCs are well-known vocal features that have been used for emotion recognition in articles such as [15], [24].…”

mentioning

confidence: 99%

A Study of Language and Classifier-independent Feature Analysis for Vocal Emotion Recognition

Noroozi,

Marjanovic,

Njegus

et al. 2018

Preprint

View full text Add to dashboard Cite

Every speech signal carries implicit information about the emotions, which can be extracted by speech processing methods. In this paper, we propose an algorithm for extracting features that are independent from the spoken language and the classification method to have comparatively good recognition performance on different languages independent from the employed classification methods. The proposed algorithm is composed of three stages. In the first stage, we propose a feature ranking method analyzing the state-of-the-art voice quality features. In the second stage, we propose a method for finding the subset of the common features for each language and classifier. In the third stage, we compare our approach with the recognition rate of the state-of-the-art filter methods. We use three databases with different languages, namely, Polish, Serbian and English. Also three different classifiers, namely, nearest neighbour, support vector machine and gradient descent neural network, are employed. It is shown that our method for selecting the most significant language-independent and method-independent features in many cases outperforms state-of-the-art filter methods.

show abstract

Non-linguistic Speech Processing

Kopparapu¹

2014

Non-Linguistic Analysis of Call Center Conversations

View full text Add to dashboard Cite

Improvement of Thai speech emotion recognition by using face feature analysis

Cited by 11 publications

References 28 publications

Speech emotion recognition using Support Vector Machines

Speech emotion recognition using Support Vector Machines

A Study of Language and Classifier-independent Feature Analysis for Vocal Emotion Recognition

Non-linguistic Speech Processing

Contact Info

Product

Resources

About