Abstract
Speech classification has been widely used in many speech-related applications. However, the complexity of speech classification tasks often exceeds the scope of non-experts, the off-the-shelf speech classification methods are urgently needed. Recently, the automatic speech classification (AutoSpeech) without any human intervention has attracted more and more attention. The practical AutoSpeech solution should be general and can automatically handle classification tasks from different domains. Moreover, AutoSpeech should improve not only the final performance but also the any-time performance especially when the time budget is limited. To address these issues, we propose a three-stage any-time learning algorithm framework called Progressive AutoSpeech for automatic speech classification under a given time budget. Progressive AutoSpeech consists of the fast stage, enhancement stage, and exploration stage. Each stage uses different models and features to ensure generalization. Additionally, we automatically construct ensembles of top-k prediction results to improve the robustness. The experimental results reveal that Progressive AutoSpeech is effective and efficient for a wide range of speech classification tasks and can achieve the best ALC score.
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Notes
- 1.
Progressive AutoSpeech won the first place in the NeurIPS 2019 AutoSpeech challenge and the second place in the Interspeech 2020 AutoSpeech challenge.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (U1811461), National Key R&D Program of China (2019YFC1711000), and Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Zhu, G. et al. (2021). Progressive AutoSpeech: An Efficient and General Framework for Automatic Speech Classification. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_14
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