Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition

Abstract

Recognizing human non-speech vocalizations is an important task and has broad applications such as automatic sound transcription and health condition monitoring. However, existing datasets have a relatively small number of vocal sound samples or noisy labels. As a consequence, state-of-the-art audio event classification models may not perform well in detecting human vocal sounds. To support research on building robust and accurate vocal sound recognition, we have created a VocalSound dataset consisting of over 21,000 crowdsourced recordings of laughter, sighs, coughs, throat clearing, sneezes, and sniffs from 3,365 unique subjects. Experiments show that the vocal sound recognition performance of a model can be significantly improved by 41.9% by adding VocalSound dataset to an existing dataset as training material. In addition, different from previous datasets, the VocalSound dataset contains meta information such as speaker age, gender, native language, country, and health condition.

arXiv commentAccepted at ICASSP 2022. Dataset and code at https://github.com/YuanGongND/vocalsound Interactive Colab demo at https://colab.research.google.com/github/YuanGongND/vocalsound/blob/main/colab/VocalSound.ipynb

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Cited by in corpus (5)

• DeFT-Mamba: Universal Multichannel Sound Separation and Polyphonic Audio Classification
• A Survey on Speech Large Language Models for Understanding
• Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource Devices
• Active Learning of Non-semantic Speech Tasks with Pretrained Models
• From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data