Titlebook: Emotion Recognition using Speech Features; K. Sreenivasa Rao,Shashidhar G. Koolagudi Book 2013 Springer Science+Business Media New York 20

連結(jié)

書目名稱Emotion Recognition using Speech Features影響因子(影響力)




書目名稱Emotion Recognition using Speech Features影響因子(影響力)學(xué)科排名




書目名稱Emotion Recognition using Speech Features網(wǎng)絡(luò)公開度




書目名稱Emotion Recognition using Speech Features網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Emotion Recognition using Speech Features被引頻次




書目名稱Emotion Recognition using Speech Features被引頻次學(xué)科排名




書目名稱Emotion Recognition using Speech Features年度引用




書目名稱Emotion Recognition using Speech Features年度引用學(xué)科排名




書目名稱Emotion Recognition using Speech Features讀者反饋




書目名稱Emotion Recognition using Speech Features讀者反饋學(xué)科排名

重力

2191-737X systems and prosodic features in order to enhance the emotion recognition performance; ? Proposed multi-stage and hybrid models for improving the emotion recognition performance. This brief is for researchers 978-1-4614-5142-6978-1-4614-5143-3Series ISSN 2191-737X Series E-ISSN 2191-7388

反叛者

Henning F. Harmuth,Konstantin A. Lukinmotions from psychological and engineering view points. Influence of emotions on the characteristics of speech production system is briefly mentioned. Role of various features extracted from excitation source, vocal tract system and prosody, are discussed in the context of developing various speech

Apraxia

Hyperalgesia

H. J. Goldschmidt D.Sc., F.Inst.P., F.I.M.urce information for emotion recognition is illustrated by demonstrating the speech files with source information alone. Details of extraction of proposed excitation source features ((i) Sequence of LP residual samples, (ii) LP residual phase, (iii) Epoch parameters and (iv) Glottal pulse parameters

銼屑

Extracellular Matrix and Cardiac Remodelingpstral coefficients (LPCCs) and mel frequency cepstral coefficients (MFCCs) are used as the correlates of vocal tract information for discriminating the emotions. In addition to LPCCs and MFCCs, formant related features are also explored in this work for recognizing emotions from speech. Extraction

銼屑

B. Stea,D. Shimm,J. Kittelson,T. Cetaseatures to recognize the emotions is illustrated using the gross statistics and time varying patterns of prosodic parameters. Global prosodic features representing the gross statistics of prosody and local prosodic features representing the finer variations in prosody are introduced in this chapter

抗體

羽毛長成

Emotion Recognition using Speech Features978-1-4614-5143-3Series ISSN 2191-737X Series E-ISSN 2191-7388

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