# Far-Field Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2009.09395 · HTML: https://mlsanity.com/paper/2009.09395/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 30 of 32)

- [Conditional Diffusion Probabilistic Model for Speech Enhancement](https://mlsanity.com/paper/2202.05256/) (2022)
- [u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality](https://mlsanity.com/paper/2207.07036/) (2022)
- [Multi-Channel Transformer Transducer for Speech Recognition](https://mlsanity.com/paper/2108.12953/) (2021)
- [Perceive and predict: self-supervised speech representation based loss functions for speech enhancement](https://mlsanity.com/paper/2301.04388/) (2023)
- [Hearing Anywhere in Any Environment](https://mlsanity.com/paper/2504.10746/) (2025)
- [A Conformer Based Acoustic Model for Robust Automatic Speech Recognition](https://mlsanity.com/paper/2203.00725/) (2022)
- [Receptive Field Analysis of Temporal Convolutional Networks for Monaural Speech Dereverberation](https://mlsanity.com/paper/2204.06439/) (2022)
- [MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Data](https://mlsanity.com/paper/2203.12369/) (2022)
- [BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition](https://mlsanity.com/paper/2505.00059/) (2025)
- [Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition](https://mlsanity.com/paper/2109.04783/) (2021)
- [Dual-Encoder Architecture with Encoder Selection for Joint Close-Talk and Far-Talk Speech Recognition](https://mlsanity.com/paper/2109.08744/) (2021)
- [UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures](https://mlsanity.com/paper/2305.20054/) (2023)
- [Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition](https://mlsanity.com/paper/2307.02909/) (2023)
- [Utterance Weighted Multi-Dilation Temporal Convolutional Networks for Monaural Speech Dereverberation](https://mlsanity.com/paper/2205.08455/) (2022)
- [Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation](https://mlsanity.com/paper/2210.15305/) (2022)
- [The Effect of Spoken Language on Speech Enhancement using Self-Supervised Speech Representation Loss Functions](https://mlsanity.com/paper/2307.14502/) (2023)
- [Speech enhancement with frequency domain auto-regressive modeling](https://mlsanity.com/paper/2309.13537/) (2023)
- [On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments](https://mlsanity.com/paper/2310.06125/) (2023)
- [Reverberation as Supervision for Speech Separation](https://mlsanity.com/paper/2211.08303/) (2022)
- [On Data Sampling Strategies for Training Neural Network Speech Separation Models](https://mlsanity.com/paper/2304.07142/) (2023)
- [Convoifilter: A case study of doing cocktail party speech recognition](https://mlsanity.com/paper/2308.11380/) (2023)
- [End-to-End Multi-speaker ASR with Independent Vector Analysis](https://mlsanity.com/paper/2204.00218/) (2022)
- [Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition](https://mlsanity.com/paper/2406.08914/) (2024)
- [Out-of-distribution generalisation in spoken language understanding](https://mlsanity.com/paper/2407.07425/) (2024)
- [A Synergistic Framework of Nonlinear Acoustic Computing and Reinforcement Learning for Real-World Human-Robot Interaction](https://mlsanity.com/paper/2505.01998/) (2025)
- [ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition](https://mlsanity.com/paper/2601.19919/) (2026)
- [Scaling sparsemax based channel selection for speech recognition with ad-hoc microphone arrays](https://mlsanity.com/paper/2103.15305/) (2021)
- [Do You Listen with One or Two Microphones? A Unified ASR Model for Single and Multi-Channel Audio](https://mlsanity.com/paper/2106.02750/) (2021)
- [Spatial Processing Front-End For Distant ASR Exploiting Self-Attention Channel Combinator](https://mlsanity.com/paper/2203.13919/) (2022)
- [RIR-SF: Room Impulse Response Based Spatial Feature for Target Speech Recognition in Multi-Channel Multi-Speaker Scenarios](https://mlsanity.com/paper/2311.00146/) (2023)

## Abstract (arXiv)

The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase of attention in science and industry, which caused or was caused by an equally significant improvement in recognition accuracy. Meanwhile it has entered the consumer market with digital home assistants with a spoken language interface being its most prominent application. Speech recorded at a distance is affected by various acoustic distortions and, consequently, quite different processing pipelines have emerged compared to ASR for close-talk speech. A signal enhancement front-end for dereverberation, source separation and acoustic beamforming is employed to clean up the speech, and the back-end ASR engine is robustified by multi-condition training and adaptation. We will also describe the so-called end-to-end approach to ASR, which is a new promising architecture that has recently been extended to the far-field scenario. This tutorial article gives an account of the algorithms used to enable accurate speech recognition from a distance, and it will be seen that, although deep learning has a significant share in the technological breakthroughs, a clever combination with traditional signal processing can lead to surprisingly effective solutions.