SCOPUS 정보 검색 플랫폼

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5398 LNAI, Issue , 2009, Pages 256-263

Recognition of emotions in German speech using gaussian mixture models

(2) Vondra, Martin a Vích, Robert a

a INSTITUTE OF PHOTONICS AND ELECTRONICS (Czech Republic)

Author keywords

Emotion recognition; Gaussian mixture models; Speech emotions

Indexed keywords

CLASSIFICATION ACCURACY; EMOTION RECOGNITION; GAUSSIAN MIXTURE MODEL; GAUSSIAN MIXTURE MODELS; MODEL COMPONENTS; OPTIMAL SELECTION; PARAMETER SET; RECOGNITION OF EMOTION; ROBUST ALGORITHM; SPEAKER RECOGNITION; SPEECH SIGNALS; SUPRASEGMENTAL FEATURES; THREE PARAMETERS;

COMMUNICATION CHANNELS (INFORMATION THEORY); FACE RECOGNITION; MAGNETOSTRICTIVE DEVICES; MIXTURES; OBJECT RECOGNITION;

SPEECH RECOGNITION;

EID: 67650474760 PISSN: 03029743 EISSN: 16113349 Source Type: Book Series
DOI: 10.1007/978-3-642-00525-1_26 Document Type: Conference Paper

Times cited : (17)

References (7)

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2
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- A Database of German Emotional Speech
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3
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- Truong, K.P., Leeuven, D.A.: An 'open-set' detection evaluation methology for automatic emotion recognition in speech. In: ParaLing 2007: Workshop on Paralinguistic Speech - between models and data, Saarbrücken, Germany (2007)
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- Truong, K.P.¹ Leeuven, D.A.²

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- Ensemble methods for spoken emotion recognition in call-centers
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5
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- Wavesurfer
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- Brookes, M.: VOICEBOX: Speech Processing Toolbox for MATLAB, http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html
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7
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- Speech emotion recognition using hidden markovov models
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.