# Streaming Noise Context Aware Enhancement For Automatic Speech Recognition in Multi-Talker Environments

arXiv: https://arxiv.org/abs/2205.08555 · HTML: https://mlsanity.com/paper/2205.08555/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 17 May 2022.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 13 of 13)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation](https://mlsanity.com/paper/1910.06379/) (2019)
- [The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines](https://mlsanity.com/paper/1803.10609/) (2018)
- [A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency](https://mlsanity.com/paper/2003.12710/) (2020)
- [VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking](https://mlsanity.com/paper/1810.04826/) (2018)
- [A Better and Faster End-to-End Model for Streaming ASR](https://mlsanity.com/paper/2011.10798/) (2020)
- [MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition](https://mlsanity.com/paper/1910.06522/) (2019)
- [Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM](https://mlsanity.com/paper/1706.02737/) (2017)
- [Exploring Neural Transducers for End-to-End Speech Recognition](https://mlsanity.com/paper/1707.07413/) (2017)
- [End-to-End Multi-speaker Speech Recognition with Transformer](https://mlsanity.com/paper/2002.03921/) (2020)
- [Toward domain-invariant speech recognition via large scale training](https://mlsanity.com/paper/1808.05312/) (2018)

## Abstract (arXiv)

One of the most challenging scenarios for smart speakers is multi-talker, when target speech from the desired speaker is mixed with interfering speech from one or more speakers. A smart assistant needs to determine which voice to recognize and which to ignore and it needs to do so in a streaming, low-latency manner. This work presents two multi-microphone speech enhancement algorithms targeted at this scenario. Targeting on-device use-cases, we assume that the algorithm has access to the signal before the hotword, which is referred to as the noise context. First is the Context Aware Beamformer which uses the noise context and detected hotword to determine how to target the desired speaker. The second is an adaptive noise cancellation algorithm called Speech Cleaner which trains a filter using the noise context. It is demonstrated that the two algorithms are complementary in the signal-to-noise ratio conditions under which they work well. We also propose an algorithm to select which one to use based on estimated SNR. When using 3 microphone channels, the final system achieves a relative word error rate reduction of 55% at -12dB, and 43% at 12dB.