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https://github.com/bagustris/apsipa2019_speechtext

Repository for code and paper submitted for APSIPA 2019, Lanzhou, China

https://github.com/bagustris/apsipa2019_speechtext

Science Score: 26.0%

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    Low similarity (9.4%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

Repository for code and paper submitted for APSIPA 2019, Lanzhou, China

Basic Info
  • Host: GitHub
  • Owner: bagustris
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 589 KB
Statistics
  • Stars: 23
  • Watchers: 1
  • Forks: 5
  • Open Issues: 9
  • Releases: 0
Created over 7 years ago · Last pushed almost 2 years ago
Metadata Files
Readme

README.md

APSIPA2019_SpeechText

Repository for code, paper, and slide ~submitted~ presented ~for~ at APSIPA 2019:

Speech emotion recognition Using Speech Feature and Word Embedding

Pre-processing:
Run the following file with some adjusments (location of IEMOCAP data, output file name, etc.).
https://github.com/bagustris/Apsipa2019SpeechText/blob/master/code/pythonfiles/mocapdatacollect.py

Main codes:
- speeh_emo.ipynb: for speech emotion recognition - text_emo.ipynb: for text emotion recognition - speech_text.ipynb: for speech and text emotion recognition (main proposal)

Other (python) files can be explored and run indepently.

In case of the jupyter notebook is not rendered by Github, see the following nbviewer instead: - https://nbviewer.jupyter.org/github/bagustris/Apsipa2019SpeechText/blob/master/code/pythonfiles/speechemo.ipynb - https://nbviewer.jupyter.org/github/bagustris/Apsipa2019SpeechText/blob/master/code/pythonfiles/textemo.ipynb - https://nbviewer.jupyter.org/github/bagustris/Apsipa2019SpeechText/blob/master/code/pythonfiles/speech_text.ipynb

By employing acoustic feature from voice parts of speech and word embedding from text we got boost accuracy of 75.49%. Here the list of obtained accuracy from different models (Text+Speech):

~~~~

Model | Accuracy (%)

Dense+Dense | 63.86 Conv1D+Dense | 68.82 LSTM+BLSTm | 69.13

LSTM+Dense | 75.49

~~~~

Sample of feature

Due to Github's limitation, a sample of feature can be downloaded here (voiced feature without SIL removal): https://cloud.degoo.com/share/Ov563dopNnEW14jNDeBig. You can use the following script inside `code/pythonfiles` directory to generate that feature file: https://github.com/bagustris/Apsipa2019SpeechText/blob/master/code/pythonfiles/save_feature.py

Citation

~~~latex B.T. Atmaja, M. Akagi, K. Shirai. "Speech Emotion Recognition from Speech Feature and Word Embedding", In Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), IEEE, 2019. ~~~

Owner

  • Name: Bagus Tris Atmaja
  • Login: bagustris
  • Kind: user
  • Location: Tsukuba
  • Company: AIST

Researcher @aistairc @VibrasticLab

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dependencies (11)

Dependencies

requirements.txt pypi
  • Keras ==2.3.1
  • gensim ==3.8.1
  • tensorflow ==1.15.5