# Towards Improved Room Impulse Response Estimation for Speech Recognition

arXiv: https://arxiv.org/abs/2211.04473 · HTML: https://mlsanity.com/paper/2211.04473/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning](https://mlsanity.com/paper/2206.08312/) (2022)
- [Few-Shot Audio-Visual Learning of Environment Acoustics](https://mlsanity.com/paper/2206.04006/) (2022)
- [Building and Evaluation of a Real Room Impulse Response Dataset](https://mlsanity.com/paper/1811.06795/) (2018)
- [FAST-RIR: Fast neural diffuse room impulse response generator](https://mlsanity.com/paper/2110.04057/) (2021)
- [GWA: A Large High-Quality Acoustic Dataset for Audio Processing](https://mlsanity.com/paper/2204.01787/) (2022)
- [MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes](https://mlsanity.com/paper/2205.09248/) (2022)
- [ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration](https://mlsanity.com/paper/2011.03706/) (2020)
- [The Cone of Silence: Speech Separation by Localization](https://mlsanity.com/paper/2010.06007/) (2020)
- [Filtered Noise Shaping for Time Domain Room Impulse Response Estimation From Reverberant Speech](https://mlsanity.com/paper/2107.07503/) (2021)
- [Recent Progresses in Deep Learning based Acoustic Models (Updated)](https://mlsanity.com/paper/1804.09298/) (2018)
- [FRA-RIR: Fast Random Approximation of the Image-source Method](https://mlsanity.com/paper/2208.04101/) (2022)
- [TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation](https://mlsanity.com/paper/2103.16849/) (2021)

## Built on by (most cited first, 9 of 9)

- [AV-RIR: Audio-Visual Room Impulse Response Estimation](https://mlsanity.com/paper/2312.00834/) (2023)
- [Listen2Scene: Interactive material-aware binaural sound propagation for reconstructed 3D scenes](https://mlsanity.com/paper/2302.02809/) (2023)
- [How Would It Sound? Material-Controlled Multimodal Acoustic Profile Generation for Indoor Scenes](https://mlsanity.com/paper/2508.02905/) (2025)
- [Gencho: Room Impulse Response Generation from Reverberant Speech and Text via Diffusion Transformers](https://mlsanity.com/paper/2602.09233/) (2026)
- [Blind Spatial Impulse Response Generation from Separate Room- and Scene-Specific Information](https://mlsanity.com/paper/2409.14971/) (2024)
- [SPEAR: Receiver-to-Receiver Acoustic Neural Warping Field](https://mlsanity.com/paper/2406.11006/) (2024)
- [VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identification](https://mlsanity.com/paper/2502.07205/) (2025)
- [Materialistic RIR: Material Conditioned Realistic RIR Generation](https://mlsanity.com/paper/2604.21119/) (2026)
- [MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation](https://mlsanity.com/paper/2605.00431/) (2026)

## Abstract (arXiv)

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).