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Autoencoders

The Experts below are selected from a list of 24804 Experts worldwide ranked by ideXlab platform

James Glass – 1st expert on this subject based on the ideXlab platform

  • Speech feature denoising and dereverberation via deep Autoencoders for noisy reverberant speech recognition
    ICASSP IEEE International Conference on Acoustics Speech and Signal Processing – Proceedings, 2014
    Co-Authors: Xue Feng, Yaodong Zhang, James Glass

    Abstract:

    Denoising Autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA is first pre-trained as restricted Boltzmann machines (RBMs) in an unsupervised fashion. Then it is unrolled to Autoencoders, and fine-tuned by corresponding clean speech features to learn a nonlinear mapping from noisy to clean features. Acoustic models are re-trained using the reconstructed features from the DDA, and speech recognition is performed. The proposed approach is evaluated on the CHiME-WSJ0 corpus, and shows a 16-25% absolute improvement on the recognition accuracy under various SNRs.

Signal Processing – 2nd expert on this subject based on the ideXlab platform

  • SPEECH FEATURE DENOISING AND DEREVERBERATION VIA DEEP Autoencoders FOR NOISY REVERBERANT SPEECH RECOGNITION Xue Feng , Yaodong Zhang , James Glass MIT Computer Science and Artificial Intelligence Laboratory
    ICASSP IEEE International Conference on Acoustics Speech and Signal Processing – Proceedings, 2014
    Co-Authors: Ieee International Conference, Signal Processing

    Abstract:

    Denoising Autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA is first pre-trained as restricted Boltz-mann machines (RBMs) in an unsupervised fashion. Then it is unrolled to Autoencoders, and fine-tuned by corresponding clean speech features to learn a nonlinear mapping from noisy to clean features. Acoustic models are retrained using the reconstructed features from the DDA, and speech recognition is performed. The proposed approach is evaluated on the CHiME-WSJ0 corpus, and shows a 16-25% absolute improvement on the recognition accuracy under various SNRs.

Xue Feng – 3rd expert on this subject based on the ideXlab platform

  • Speech feature denoising and dereverberation via deep Autoencoders for noisy reverberant speech recognition
    ICASSP IEEE International Conference on Acoustics Speech and Signal Processing – Proceedings, 2014
    Co-Authors: Xue Feng, Yaodong Zhang, James Glass

    Abstract:

    Denoising Autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA is first pre-trained as restricted Boltzmann machines (RBMs) in an unsupervised fashion. Then it is unrolled to Autoencoders, and fine-tuned by corresponding clean speech features to learn a nonlinear mapping from noisy to clean features. Acoustic models are re-trained using the reconstructed features from the DDA, and speech recognition is performed. The proposed approach is evaluated on the CHiME-WSJ0 corpus, and shows a 16-25% absolute improvement on the recognition accuracy under various SNRs.