# Blind Signal Dereverberation for Machine Speech Recognition

arXiv: https://arxiv.org/abs/2210.00117 · HTML: https://mlsanity.com/paper/2210.00117/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Radically Old Way of Computing Spectra: Applications in End-to-End ASR](https://mlsanity.com/paper/2103.14129/) (2021)
- [Complex Frequency Domain Linear Prediction: A Tool to Compute Modulation Spectrum of Speech](https://mlsanity.com/paper/2203.13216/) (2022)

## Abstract (arXiv)

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.