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Emotional Speech Recognition.pdf

201512_SIGSLP_Mukaihara_1.paper.pdf
Preview of Emotional Speech Recognition
🔗 Source: ahcweb01.naist.jp
📊 Size: 1.34 MB
📄 Pages: 7 pages
⬇️ Downloads: 199

Summary

Emotion influences speech and degrades ASR quality due to mismatch between input speech and acoustic model. This study proposes using DNN and CNN bottleneck features for emotional speech recognition to improve accuracy. Bottleneck features are extracted from a deep neural network with a smaller number of nodes in the middle layer, which can extract robust acoustic features. The study combines bottleneck features with other feature transformation methods, such as LDA and fMLLR, to further improve recognition accuracy. Experiments using the Kaldi toolkit and Japanese spontaneous speech corpus show that DNN and CNN bottleneck features improve recognition accuracy compared to baseline systems.

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Description

Emotion influences speech and degrades ASR quality due to mismatch between input speech and acoustic model.

Technical Information

  • File Format: PDF
  • File Size: 1.34 MB
  • Pages: 7
  • Language: EN
  • Total Downloads: 199
  • Last Updated: 4 days ago

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This PDF document about Emotional Speech Recognition provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Emotional Speech Recognition.

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Emotional guideEmotional tutorialSpeech guideSpeech tutorialRecognition guideRecognition tutorial

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