Fuzzy Support Vector Machine-Based Emotional Optimal Algorithm in Spoken Chinese

Qin, Yuqiang; Zhang, Xueying

doi:10.1166/jctn.2012.2270

Cited by 4 publications

(3 citation statements)

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“…This is because together they are sufficient to ''support" the optimal separating hyperplane, since only SVs are such that 0 > i α . It follows that the decision rule can be expressed simply in terms of SVs [11] [12].…”

Section: Support Vector Machine (Svm) Classifiermentioning

confidence: 99%

SVM-Based Credit Rating and Feature Selection

Qin

Ying

2014

AMM

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The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit rating for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines (SVM) against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default.

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Section: Support Vector Machine (Svm) Classifiermentioning

confidence: 99%

SVM-Based Credit Rating and Feature Selection

Qin

Ying

2014

AMM

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“…Based on these characteristics, WSN has been applied in many fields, such as environmental measurement, medical, industrial and military applications [2]. Therefore, many researchers have proposed a series of localization algorithms [3] [4]. The common feature of all localization algorithms is using such a priori knowledge about the location information of the beacon nodes to estimate the unknown node location.…”

Section: Introductionmentioning

confidence: 99%

PGSA-Based Localization Algorithm for Wireless Sensor Network

Qin

Ying

2016

International Journal on Smart Sensing and Intelligent Systems

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This paper proposes a novel localization algorithm for wireless sensor network (WSN). Accurate localization is very important for WSN. WSN localization problem is sometimes regarded as an optimization problem. Plant growth simulation algorithm (PGSA) is a kind of new intelligent optimization algorithm, which is intelligent simulation of plant growth in natural way. In addition to the common characteristics of intelligent algorithms, PGSA show robustness and provides a global optimal solution, etc. In this paper, further enhancement of the algorithm by adding the plant root of adaptive backlight function to effectively improve the computing speed and localization precision has been reported. Comparing this algorithm with simulated annealing algorithm (SAA), simulation resultsshow that this algorithm has a higher and more consistent localization precision and faster computational speed.

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“…Affective human computer interaction has been the focus of artificial intelligence research for several years now, and the research has moving ahead from the simple information exchange between human and computer towards the affective communication [1]. Affective human computer interaction technology could be widely applied in virtual reality, especially in the field of entertainment and games.…”

Section: Introductionmentioning

confidence: 99%

EEMD-Based Speaker Automatic Emotional Recognition in Chinese Mandarin

Qin¹,

Qi²

2014

Appl. Math. Inf. Sci.

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Emotion feature extraction is the key to speech emotional recognition. And ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating emotion mode mixing present in the original empirical mode decomposition(EMD). To evaluate the performance of this new method, this paper investigates the effect of a parameters pertinent to EEMD: speech emotional envelope. Firstly, a speaker emotional envelope features extraction based on EEMD is proposed in the paper. Using the piecewise power function in speech emotional envelope has a better effect in emotional identification. At the same time, the proposed technique has been utilized for classification of four kinds of emotional(angry, happy, sad and neutral) speech signals. Emotional intrinsic mode functions(IMFe) are obtained by empirical mode decomposition on emotional speech signals, the fast fourier transform(FFT) of each intrinsic mode function is extracted as the emotional feature coefficient which is used in speaker emotional identification applying by vector quantization. MATLAB is used to calculate the characteristic of emotional speech signals using empirical mode decomposition (EEMD). We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop feature vectors. The results indicate the proposed method works well in speaker emotional identification.

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Fuzzy Support Vector Machine-Based Emotional Optimal Algorithm in Spoken Chinese

Cited by 4 publications

References 0 publications

SVM-Based Credit Rating and Feature Selection

SVM-Based Credit Rating and Feature Selection

PGSA-Based Localization Algorithm for Wireless Sensor Network

EEMD-Based Speaker Automatic Emotional Recognition in Chinese Mandarin

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