multimodal-speech-emotion

This repository contains the source code used in the following paper,

Multimodal Speech Emotion Recognition using Audio and Text, IEEE SLT-18, [paper]

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[requirements]

tensorflow==1.4 (tested on cuda-8.0, cudnn-6.0)
python==2.7
scikit-learn==0.20.0
nltk==3.3

[download data corpus]

• IEMOCAP [link] [paper]
• download IEMOCAP data from its original web-page (license agreement is required)

[preprocessed-data schema (our approach)]

• Get the preprocessed dataset [application link]

  If you want to download the "preprocessed dataset," please ask the license to the IEMOCAP team first.

• for the preprocessing, refer to codes in the "./preprocessing"

• We cannot publish ASR-processed transcription due to the license issue (commercial API), however, we assume that it is moderately easy to extract ASR-transcripts from the audio signal by oneself. (we used google-cloud-speech-api)

• Format of the data for our experiments:
  

  MFCC : MFCC features of the audio signal (ex. train_audio_mfcc.npy)
  [#samples, 750, 39] - (#sampels, sequencs(max 7.5s), dims)
  

  MFCC-SEQN : valid lenght of the sequence of the audio signal (ex. train_seqN.npy)
  [#samples] - (#sampels)
  

  PROSODY : prosody features of the audio signal (ex. train_audio_prosody.npy)
  [#samples, 35] - (#sampels, dims)
  

  TRANS : sequences of trasnciption (indexed) of a data (ex. train_nlp_trans.npy)
  [#samples, 128] - (#sampels, sequencs(max))
  

  LABEL : targe label of the audio signal (ex. train_label.npy)
  [#samples] - (#sampels)
  

[source code]

• repository contains code for following models

  Audio Recurrent Encoder (ARE)
  Text Recurrent Encoder (TRE)
  Multimodal Dual Recurrent Encoder (MDRE)
  Multimodal Dual Recurrent Encoder with Attention (MDREA)
  

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[training]

• refer "reference_script.sh"
• fianl result will be stored in "./TEST_run_result.txt"
  

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[cite]

• Please cite our paper, when you use our code | model | dataset

  @inproceedings{yoon2018multimodal,
  title={Multimodal Speech Emotion Recognition Using Audio and Text},
  author={Yoon, Seunghyun and Byun, Seokhyun and Jung, Kyomin},
  booktitle={2018 IEEE Spoken Language Technology Workshop (SLT)},
  pages={112--118},
  year={2018},
  organization={IEEE}
  }