jiminbot20 / GLAM

This is the official code for paper "Speech Emotion Recognition with Global-Aware Fusion on Multi-scale Feature Representation" published in ICASSP 2022

코드 재현

1. path.py

• datasets_path = '내 데이터 경로' 이것만 설정하게 되어있는데
• IEMOCAP = '내 데이터 경로' 이 코드 라인도 추가해야 전처리 코드 handleIEMOCAP.py 돌아감.

2. 전처리 - handleIEMOCAP.py 돌리기

3. ./scripts/돌리고싶은 shell

GLAM

By Wenjing Zhu, Xiang Li.

Du Xiaoman

Introduction

GLobal-Aware Multi-scale (GLAM) is a novel neural network to learn multi-scale feature representation with global-aware fusion module to attend emotional information for Speech Emotion Recognition (SER).

This repository contains the implementation used for the results in our paper.

Requirements and Installation

• Python version 3.6
• PyTorch
• g_mlp_pytorch
• area_attention
• pytorch_model_summary

Preprocess

1. specify path of IEMOCAP corpus in path.py
2. run python handleIEMOCAP.py to rename audio filename.
3. run python seeds.py to generate seeds.

Training

Use python train.py to train a new model.

Run directly: ./run(to make the script executable: chmod +x run) or bash run.

Training scripts are stored in scripts directory. Please move the script to root directory in this repository before execute it.

GLAM

• training on improvised dataset.

  ./GLAM

• training on scripted dataset.

  ./GLAM_script

• training on full dataset.

  ./GLAM_all

MACNN

• training on improvised dataset.

  ./MACNN

• training on scripted dataset.

  ./MACNN_script

• training on full dataset.

  ./MACNN_all

CNN + Attention pooling

• training on improvised dataset.

  ./attnpooling

• training on scripted dataset.

  ./attnpooling_script

• training on full dataset.

  ./attnpooling_all

Ablation study

• Multi-scale

  ./ResMultiConv3

• Multi-scale + MHA

  ./MHResMultiConv3

• Multi-scale + AA

  ./AAResMultiConv3

• MHCNN

  ./MHCNN

• AACNN(TF2 implementatiion)

  see https://github.com/lessonxmk/Optimized_attention_for_SER

Results

Confusion matrix on Improvisation dataset. a) and b) represent results of AACNN and GLAM model respectively.

Visualizations of hidden states by commonly-used t- SNE method on improvisation dataset for four models: a) APCNN; b) MHCNN; c) AACNN; d) GLAM.

Comparison of evaluation metrics on three types of datasets.

Dataset	Model	WA	UA	macro F1	micro F1
Improvisation	APCNN	70.73+/-2.57	67.96+/-2.66	68.26+/-3.17	70.20+/-3.00
Improvisation	MHCNN	76.09+/-2.01	73.87+/-2.31	74.27+/-2.25	75.91+/-2.05
Improvisation	AACNN	78.47+/-2.42	76.68+/-3.29	76.69+/-3.27	78.29+/-2.58
Improvisation	GLAM	81.18+/-1.47	79.25+/-1.88	79.87+/-1.64	80.99+/-1.50
Script	APCNN	55.95+/-2.93	54.86+/-2.74	48.75+/-3.71	51.15+/-3.87
Script	MHCNN	64.57+/-2.12	63.42+/-2.14	61.46+/-2.50	63.36+/-2.56
Script	AACNN	67.20+/-4.78	65.94+/-6.19	64.82+/-7.63	66.55+/-6.99
Script	GLAM	71.44+/-2.05	70.39+/-2.13	69.56+/-2.13	70.91+/-2.12
Full	APCNN	62.53+/-1.43	62.80+/-1.53	61.89+/-1.52	62.12+/-1.50
Full	MHCNN	69.80+/-1.69	70.09+/-1.74	69.65+/-1.73	69.68+/-1.71
Full	AACNN	70.94+/-5.08	71.04+/-4.84	70.59+/-6.34	70.71+/-6.44
Full	GLAM	73.70+/-1.25	73.90+/-1.31	73.51+/-1.29	73.60+/-1.27

Performance of multi-scale module and global- aware module on Improvisation dataset.

Model	WA	UA	macro F1	micro F1
GLAM	81.18+/-1.47	79.25+/-1.88	79.87+/-1.64	80.99+/-1.50
Multi-scale	80.89 +/- 1.43	78.85 +/- 1.65	79.47 +/- 1.60	80.67 +/- 1.45
Multi-scale + MHA	80.43 +/- 1.53	78.92 +/- 1.92	79.20 +/- 1.68	80.26 +/- 1.53
Multi-scale + AA	80.61 +/- 1.75	79.09 +/- 1.75	79.47 +/- 1.80	80.46 +/- 1.77
MHCNN	76.09 +/- 2.01	73.87 +/- 2.31	74.27 +/- 2.25	75.91 +/- 2.05
AACNN	78.47 +/- 2.42	76.68 +/- 3.29	76.69 +/- 3.27	78.29 +/- 2.58

Result of (\alpha) on Improvisation dataset.

(\alpha)	WA	UA	macro F1	micro F1
0	80.44 +/- 1.54	78.88 +/- 1.66	79.31 +/- 1.55	80.32 +/- 1.55
0.3	81.15 +/- 1.73	79.26 +/- 1.98	79.88 +/- 1.85	80.98 +/- 1.76
0.5	81.18+/-1.47	79.25+/-1.88	79.87+/-1.64	80.99+/-1.50
0.8	80.97 +/- 1.53	78.97 +/- 1.85	79.62 +/- 1.75	80.79 +/- 1.53
1	80.96 +/- 1.72	79.19 +/- 1.92	79.65 +/- 1.88	80.78 +/- 1.73
2	80.80 +/- 1.39	79.18 +/- 1.66	79.55 +/- 1.50	80.64 +/- 1.40
3	80.61 +/- 1.45	78.95 +/- 1.58	79.26 +/- 1.59	80.44 +/- 1.45

Split the dataset into training, validation and testing sets at a ratio of 8:1:1.

Dataset	Model	WA	UA	macro F1	micro F1
Improvisation	APCNN	69.93 +/- 4.33	62.70 +/- 5.16	63.04 +/- 5.94	68.86 +/- 5.48
Improvisation	MHCNN	76.13 +/- 2.80	71.15 +/- 4.11	71.91 +/- 3.92	75.92 +/- 2.87
Improvisation	AACNN	78.65+/-3.30	74.31+/-4.59	74.78+/-4.43	78.47+/-3.40
Improvisation	GLAM	81.04 +/- 2.65	75.89 +/- 3.94	76.79 +/- 3.78	80.78 +/- 2.75
Script	APCNN	53.48 +/- 3.83	55.77 +/- 3.60	48.14 +/- 4.27	48.10 +/- 4.90
Script	MHCNN	63.03 +/- 3.52	64.37 +/- 3.24	61.32 +/- 3.88	61.77 +/- 4.31
Script	AACNN	65.49+/-3.49	65.87+/-2.98	64.36+/-3.42	65.22+/-3.77
Script	GLAM	69.80 +/- 3.16	70.63 +/- 3.17	68.85 +/- 3.40	69.30 +/- 3.42
Full	APCNN	60.33 +/- 2.67	62.16 +/- 2.55	60.03 +/- 2.78	59.67 +/- 2.88
Full	MHCNN	67.67 +/- 2.34	68.65 +/- 2.32	67.55 +/- 2.34	67.58 +/- 2.36
Full	AACNN	67.71+/-2.25	68.67+/-2.22	67.62+/-2.32	67.46+/-2.33
Full	GLAM	71.63 +/- 2.12	72.56 +/- 2.05	71.53 +/- 2.16	71.42 +/- 2.19

Citation

If you use this method or this code in your paper, then please cite it:

@INPROCEEDINGS{
GLAM,
title={Speech Emotion Recognition with Global-Aware Fusion on Multi-scale Feature Representation},
booktitle={Proceedings of the 2022 {IEEE} International Conference on Acoustics, Speech and Signal Processing}
author={Wenjing Zhu, Xiang Li},
publisher = {{IEEE}},
journal={ICASSP},
year={2022},
url={https://github.com/lixiangucas01/GLAM},
}