ArticleSensors (Basel, Switzerland)2022
Effects of Data Augmentations on Speech Emotion Recognition.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 35 citations in OpenAlex.
- Analysis and Research on Spectrogram-Based Emotional Speech Signal Augmentation Algorithm.Entropy (Basel, Switzerland) · 2025Article
- Automated segmentation of dental restorations using deep learning: exploring data augmentation techniques.Oral radiology · 2025Article
- Voice analysis and deep learning for detecting mental disorders in pregnant women: a cross-sectional study.Discover mental health · 2025Article
- An improved data augmentation approach and its application in medical named entity recognition.BMC medical informatics and decision making · 2024Article
- Emotion Detection Based on Pupil Variation.Healthcare (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Data augmentation techniques have recently gained more adoption in speech processing, including speech emotion recognition. Although more data tend to be more effective, there may be a trade-off in which more data will not provide a better model. This paper reports experiments on investigating the effects of data augmentation in speech emotion recognition. The investigation aims at finding the most useful type of data augmentation and the number of data augmentations for speech emotion recognition in various conditions. The experiments are conducted on the Japanese Twitter-based emotional speech and IEMOCAP datasets. The results show that for speaker-independent data, two data augmentations with glottal source extraction and silence removal exhibited the best performance among others, even with more data augmentation techniques. For the text-independent data (including speaker and text-independent), more data augmentations tend to improve speech emotion recognition performances. The results highlight the trade-off between the number of data augmentations and the performance of speech emotion recognition showing the necessity to choose a proper data augmentation technique for a specific condition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.