Blind Signal Dereverberation for Machine Speech Recognition

We present a method to remove unknown convolutive noise introduced to speech by reverberations of recording environments, utilizing some amount of training speech data from the reverberant environment, and any available non-reverberant speech data. Using Fourier transform computed over long temporal...

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Bibliographic Details
Published inarXiv.org
Main Authors Sadhu, Samik, Hermansky, Hynek
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.09.2022
Subjects
Automatic speech recognition
Domains
Fourier transforms
Impulse response
Spectra
Spectrograms
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Summary:We present a method to remove unknown convolutive noise introduced to speech by reverberations of recording environments, utilizing some amount of training speech data from the reverberant environment, and any available non-reverberant speech data. Using Fourier transform computed over long temporal windows, which ideally cover the entire room impulse response, we convert room induced convolution to additions in the log spectral domain. Next, we compute a spectral normalization vector from statistics gathered over reverberated as well as over clean speech in the log spectral domain. During operation, this normalization vectors are used to alleviate reverberations from complex speech spectra recorded under the same reverberant conditions . Such dereverberated complex speech spectra are used to compute complex FDLP-spectrograms for use in automatic speech recognition.
ISSN:2331-8422