广西师范大学学报（自然科学版） ›› 2021, Vol. 39 ›› Issue (3): 20-26.doi: 10.16088/j.issn.1001-6600.2020051802

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基于端到端深度神经网络的语音情感识别研究

吕惠炼, 胡维平*   

  1. 广西师范大学 电子工程学院, 广西 桂林 541004
  • 收稿日期:2020-05-18 修回日期:2020-10-17 发布日期:2021-05-13
  • 通讯作者: 胡维平(1963—),男,广西桂林人,广西师范大学教授,博士。E-mail: huwp@gxnu.edu.cn
  • 基金资助:
    国家自然科学基金(61861005)

Research on Speech Emotion Recognition Based on End-to-End Deep Neural Network

LÜ Huilian, HU Weiping*   

  1. College of Electronic Engineering, Guangxi Normal Nniversity, Guilin Guangxi 541004, China
  • Received:2020-05-18 Revised:2020-10-17 Published:2021-05-13

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摘要： 语音情感识别是实现自然人机交互的重要组成部分,传统语音情感识别系统主要集中于特征提取和模型构建。本文提出一种将深度神经网络直接应用于原始信号的语音情感识别方法。原始语音数据携带了语音信号的情感信息、二维空间信息和时序上下文信息。建立的模型以端到端的方式进行训练,网络自动学习原始语音信号的特征表示,无需手工特征提取步骤。该网络模型同时兼顾了CNN和BLSTM 2种神经网络的优点。利用CNN从原始语音数据中学习空间特征,其后添加一个BLSTM学习上下文特征。为了评估该模型的有效性,在IEMOCAP数据库上进行识别测试,获得的WA和UA分别为71.39%、61.06%。此外,与基线模型进行对比,验证了提出方法的有效性。

关键词: 语音情感识别, CNN, BLSTM, 端到端, 原始语音

Abstract: Speech emotion recognition is an important part of natural human-computer interaction. The traditional speech emotion recognition system mainly focuses on feature extraction and model construction. This paper proposes a speech emotion recognition method that directly applies deep neural network to the raw signal. The raw speech data carry the emotional information, two-dimensional spatial information and temporal context information of the speech signal. The model proposed is trained in an end-to-end manner, and the network automatically learns the feature representation of the raw speech signal without the need for manual feature extraction. The network model takes into account the advantages of both CNN and BLSTM neural networks. CNN is used to learn spatial features from the raw speech data, and then a BLSTM learning context feature is added. In order to evaluate the effectiveness of the model, recognition tests are carried out on IEMOCAP database, and the WA and UA obtained are 71.39% and 61.06% respectively. In addition, compared with the baseline model, the effectiveness of the proposed method is verified.

Key words: speech emotion recognition, CNN, BLSTM, end-to-end, raw speech

中图分类号: 

  • TN912.34
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