Abstract
A new neural network approach to speech characteristic features extraction is proposed. The features provide separable information about the pitch of a speech signal and amplitude spectral envelope. The features can compactly describe a speech signal and allows effectively training machine learning models that use the proposed features as an input. The experimental application to the voice activity detection (VAD) problem shows the advantage of the proposed features over the features that are widely used: melspectrogram, amplitude spectrum and MFCC. Experimental results show that a VAD based on proposed features is more accurate while using a simpler architecture and much less number of training parameters. The performance has been tested on noisy speech with different SNR ratios and the results show that the proposed features are more robust to noise. The experimental results indicate that the proposed VAD model outperforms the VAD from the WebRTC framework.
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Vashkevich, R., Azarov, E. (2020). Robust Noisy Speech Parameterization Using Convolutional Neural Networks. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2020. Lecture Notes in Computer Science(), vol 12335. Springer, Cham. https://doi.org/10.1007/978-3-030-60276-5_58
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