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ORVAE: One-Class Residual Variational Autoencoder for Voice Activity Detection in Noisy Environment

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Abstract

Detecting human speech is foundational for a wide range of emerging intelligent applications. However, accurately detecting human speech is challenging, especially in the presence of unknown noise patterns. Generally, deep learning-based methods have shown to be more robust and accurate than statistical methods and other existing approaches. However, typically creating a noise-robust and more generalized deep learning-based voice activity detection system requires the collection of an enormous amount of annotated audio data. In this work, we develop a generalized model trained on limited types of human speeches with noisy backgrounds. Yet, it can detect human speech in the presence of various unseen noise types, which were not present in the training set. To achieve this, we propose a one-class residual connections-based variational autoencoder (ORVAE), which only requires a limited number of human speech data with noisy background for training, thereby eliminating the need for collecting data with diverse noise patterns. Evaluating ORVAE with three different datasets (synthesized TIMIT and NOISEX-92, synthesized LibriSpeech and NOISEX-92, and a Publicly Recorded dataset), our method outperforms other one-class baseline methods, achieving \(F_1\)-scores of over \(90\%\) for multiple signal-to-noise ratio levels.

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Acknowledgements

This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) Grant funded by the Korea government (MSIT) (No.2019-0-00421, AI Graduate School Support Program (Sungkyunkwan University)), (No. 2019-0-01343, Regional strategic industry convergence security core talent training business) and the Basic Science Research Program through National Research Foundation of Korea (NRF) Grant funded by Korea government MSIT (No. 2020R1C1C1006004). Also, this research was partly supported by IITP Grant funded by the Korea government MSIT (No. 2021-0-00066 and 2021-0-00017, Core Technology Development of Artificial Intelligence Industry), and was partly supported by the MSIT (Ministry of Science, ICT), Korea, under the High-Potential Individuals Global Training Program (2020-0-01550) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation)

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Authors and Affiliations

  1. College of Computing and Informatics, Sungkyunkwan University, Suwon, Korea

    Hasam Khalid & Shahroz Tariq

  2. College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea

    TaeSoo Kim & Jong Hwan Ko

  3. Department of Applied Data Science, Sungkyunkwan University, Suwon, Korea

    Simon S. Woo

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  1. Hasam Khalid
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Correspondence to Jong Hwan Ko or Simon S. Woo.

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Khalid, H., Tariq, S., Kim, T. et al. ORVAE: One-Class Residual Variational Autoencoder for Voice Activity Detection in Noisy Environment. Neural Process Lett 54, 1565–1586 (2022). https://doi.org/10.1007/s11063-021-10695-4

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