Development of simulated emotion speech database for excitation source analysis

Pravena, D.; Govind, D.

doi:10.1007/s10772-017-9407-3

Cited by 18 publications

(6 citation statements)

References 30 publications

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“…Pravena and Govind [9] performed an exclusive analysis of the excitation source. Tamil and Malayalam emotional speech corpuses were developed for the analysis.…”

Section: IImentioning

confidence: 99%

“…If a classifier produces probabilistic outputs that indicate the probabilities a test instance belongs to an emotional class, then the probabilistic outputs from multiple classifiers can be combined in an efficient manner to accomplish improved accuracy in predicting the target classes. Support Vector Machines were used in several works for emotion recognition from speech, [6], [8], [9], [10], [19], [20], [21]. But the SVMs are not used with probabilistic outputs.…”

Section: Probabilistic Svm For Speech Emotion Recognitionmentioning

confidence: 99%

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Affect Recognition in Human Emotional Speech using Probabilistic Support Vector Machines

Nelapati

Selvarajan

2022

IJRITCC

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The problem of inferring human emotional state automatically from speech has become one of the central problems in Man Machine Interaction (MMI). Though Support Vector Machines (SVMs) were used in several worksfor emotion recognition from speech, the potential of using probabilistic SVMs for this task is not explored. The emphasis of the current work is on how to use probabilistic SVMs for the efficient recognition of emotions from speech. Emotional speech corpuses for two Dravidian languages- Telugu & Tamil- were constructed for assessing the recognition accuracy of Probabilistic SVMs. Recognition accuracy of the proposed model is analyzed using both Telugu and Tamil emotional speech corpuses and compared with three of the existing works. Experimental results indicated that the proposed model is significantly better compared with the existing methods.

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“…Pravena and Govind [9] performed an exclusive analysis of the excitation source. Tamil and Malayalam emotional speech corpuses were developed for the analysis.…”

Section: IImentioning

confidence: 99%

Section: Probabilistic Svm For Speech Emotion Recognitionmentioning

confidence: 99%

Affect Recognition in Human Emotional Speech using Probabilistic Support Vector Machines

Nelapati

Selvarajan

2022

IJRITCC

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“…6 professional actors (3 female and 3 male) were recorded in 14 Italian sentences. [34] formulatedmultilingual simulated database, consisting of Indian English, Tamil and Malayalam. The database contains of three emotion speeches such as sad, angry and happy.…”

Section: Italian Emotion Speech Database Idbmentioning

confidence: 99%

A critical insight into multi-languages speech emotion databases

Qadri¹,

Gunawan²,

Alghifari³

et al. 2019

Bulletin EEI

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With increased interest of human-computer/human-human interactions, systems deducing and identifying emotional aspects of a speech signal has emerged as a hot research topic. Recent researches are directed towards the development of automated and intelligent analysis of human utterances. Although numerous researches have been put into place for designing systems, algorithms, classifiers in the related field; however the things are far from standardization yet. There still exists considerable amount of uncertainty with regard to aspects such as determining influencing features, better performing algorithms, number of emotion classification etc. Among the influencing factors, the uniqueness between speech databases such as data collection method is accepted to be significant among the research community. Speech emotion database is essentially a repository of varied human speech samples collected and sampled using a specified method. This paper reviews 34 `speech emotion databases for their characteristics and specifications. Furthermore critical insight into the imitational aspects for the same have also been highlighted.

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“…To have a speech database is essential in process of speech emotion recognition as shown in Figure 1 [5]. Researchers and scientists have developed speech corpora invarious languages like English, German, Chinese, Spanish, Japanese, Russian, Swedish, and Italian etc [6].…”

Section: Introductionmentioning

confidence: 99%

Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters

Tank

Hadia

2020

IJECE

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In the last couple of years emotion recognition has proven its significance in the area of artificial intelligence and man machine communication. Emotion recognition can be done using speech and image (facial expression), this paper deals with SER (speech emotion recognition) only. For emotion recognition emotional speech database is essential. In this paper we have proposed emotional database which is developed in Gujarati language, one of the official’s language of India. The proposed speech corpus bifurcate six emotional states as: sadness, surprise, anger, disgust, fear, happiness. To observe effect of different emotions, analysis of proposed Gujarati speech database is carried out using efficient speech parameters like pitch, energy and MFCC using MATLAB Software.

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Development of simulated emotion speech database for excitation source analysis

Cited by 18 publications

References 30 publications

Affect Recognition in Human Emotional Speech using Probabilistic Support Vector Machines

Affect Recognition in Human Emotional Speech using Probabilistic Support Vector Machines

A critical insight into multi-languages speech emotion databases

Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters

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