Automatically augmenting an emotion dataset improves classification using audio
In this work, we tackle a problem of speech emotion classification. One of the issues in the area of affective computation is that the amount of annotated data is very limited. On the other hand, the number of ways that the same emotion can be expressed verbally is enormous due to variability between speakers. This is one of the factors that limits performance and generalization. We propose a simple method that extracts audio samples from movies using textual sentiment analysis. As a result, it is possible to automatically construct a larger dataset of audio samples with positive, negative emotional and neutral speech. We show that pretraining recurrent neural network on such a dataset yields better results on the challenging EmotiW corpus. This experiment shows a potential benefit of combining textual sentiment analysis with vocal information.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationEmotion ClassificationGeneral ClassificationSentiment AnalysisSimilar Papers 제목 키워드 기반
Semantically Enriching Investor Micro-blogs for Opinion-Aware Emotion Analysis: A Practical Approach
While sentiment analysis is the staple of financial NLP, capturing the nuances of 'why' behind that sentiment remains a challenge. There have been attempts to address this by analysing investor emotions alongside sentime…
Sentiment AnalysisLearning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks
Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms. The subtle changes in the voice of the speaker that are indicative of positive or negative…
BIG-bench Machine LearningGeneral ClassificationGenerative Adversarial NetworkRepresentation LearningSemi-Automatic Construction and Refinement of an Annotated Corpus for a Deep Learning Framework for Emotion Classification
In the case of using a deep learning (machine learning) framework for emotion classification, one significant difficulty faced is the requirement of building a large, emotion corpus in which each sentence is assigned emo…
ClassificationEmotion ClassificationGeneral ClassificationSentenceEvaluating Deep Music Generation Methods Using Data Augmentation
Despite advances in deep algorithmic music generation, evaluation of generated samples often relies on human evaluation, which is subjective and costly. We focus on designing a homogeneous, objective framework for evalua…
Data AugmentationGenre classificationMusic GenerationMusic Genre ClassificationEmotion and Sentiment Guided Paraphrasing
Paraphrase generation, a.k.a. paraphrasing, is a common and important task in natural language processing. Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has m…
Paraphrase GenerationText Generation