Curriculum Learning for Speech Emotion Recognition From Crowdsourced Labels
Curriculum Learning for Speech Emotion Recognition From Crowdsourced Labels
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DOI：
10.1109/taslp.2019.2898816
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发表时间：
2019-04-01
影响因子：
5.4
通讯作者：
Busso, Carlos
中科院分区：
文献类型：
--
作者：
Lotfian, Reza;Busso, Carlos
本研究介绍了一种设计机器学习课程的方法，以最大限度地提高深度神经网络（DNN）在语音情感识别训练过程中的效率。以前在其他机器学习问题中的研究已经表明，按照课程训练分类器的好处，其中样本以逐渐增加的难度呈现。对于语音情感识别，挑战在于在训练集中建立自然的难度顺序以创建课程。我们解决这个问题的假设，模糊的人类样本也是模糊的计算机。语音样本通常由多个评估者进行注释，以说明个体之间情感感知的差异。虽然一些具有明确情感内容的句子被一致地注释，但具有更模糊情感内容的句子在个体评价之间存在重要分歧。我们建议使用评估者之间的分歧作为衡量分类任务的难度。我们提出的指标，量化的评价间的协议，以定义回归问题和二进制和多类分类问题的课程。实验结果一致表明，依赖于基于人类判断之间的一致性的课程，与没有课程训练的基线相比，在统计上有显著的改善。
This study introduces a method to design a curriculum for machine-learning to maximize the efficiency during the training process of deep neural networks (DNNs) for speech emotion recognition. Previous studies in other machine-learning problems have shown the benefits of training a classifier following a curriculum where samples are gradually presented in increasing level of difficulty. For speech emotion recognition, the challenge is to establish a natural order of difficulty in the training set to create the curriculum. We address this problem by assuming that, ambiguous samples for humans are also ambiguous for computers. Speech samples are often annotated by multiple evaluators to account for differences in emotion perception across individuals. While some sentences with clear emotional content are consistently annotated, sentences with more ambiguous emotional content present important disagreement between individual evaluations. We propose to use the disagreement between evaluators as a measure of difficulty for the classification task. We propose metrics that quantify the inter-evaluation agreement to define the curriculum for regression problems and binary and multi-class classification problems. The experimental results consistently show that relying on a curriculum based on agreement between human judgments leads to statistically significant improvements over baselines trained without a curriculum.