Towards Improved Room Impulse Response Estimation for Speech Recognition
Abstract
We propose a novel approach for blind room impulse response (RIR) estimation systems in the context of a downstream application scenario, far-field automatic speech recognition (ASR). We first draw the connection between improved RIR estimation and improved ASR performance, as a means of evaluating neural RIR estimators. We then propose a generative adversarial network (GAN) based architecture that encodes RIR features from reverberant speech and constructs an RIR from the encoded features, and uses a novel energy decay relief loss to optimize for capturing energy-based properties of the input reverberant speech. We show that our model outperforms the state-of-the-art baselines on acoustic benchmarks (by 17\% on the energy decay relief and 22\% on an early-reflection energy metric), as well as in an ASR evaluation task (by 6.9\% in word error rate).
Cite
@article{arxiv.2211.04473,
title = {Towards Improved Room Impulse Response Estimation for Speech Recognition},
author = {Anton Ratnarajah and Ishwarya Ananthabhotla and Vamsi Krishna Ithapu and Pablo Hoffmann and Dinesh Manocha and Paul Calamia},
journal= {arXiv preprint arXiv:2211.04473},
year = {2023}
}
Comments
Accepted at ICASSP 2023. More results are available at https://anton-jeran.github.io/S2IR/