# Implicit Acoustic Echo Cancellation for Keyword Spotting and Device-Directed Speech Detection

arXiv: https://arxiv.org/abs/2111.10639 · HTML: https://mlsanity.com/paper/2111.10639/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 20 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition](https://mlsanity.com/paper/2004.08531/) (2020)
- [Device-directed Utterance Detection](https://mlsanity.com/paper/1808.02504/) (2018)
- [Exploring attention mechanism for acoustic-based classification of speech utterances into system-directed and non-system-directed](https://mlsanity.com/paper/1902.00570/) (2019)
- [Improving Device Directedness Classification of Utterances with Semantic Lexical Features](https://mlsanity.com/paper/2010.01949/) (2020)
- [Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation](https://mlsanity.com/paper/2011.15003/) (2020)
- [Textual Echo Cancellation](https://mlsanity.com/paper/2008.06006/) (2020)
- [A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer And Large Scale Synthetic Data](https://mlsanity.com/paper/2106.00856/) (2021)

## Built on by (most cited first, 2 of 2)

- [Multimodal Data and Resource Efficient Device-Directed Speech Detection with Large Foundation Models](https://mlsanity.com/paper/2312.03632/) (2023)
- [Selective Attention System (SAS): Device-Addressed Speech Detection for Real-Time On-Device Voice AI](https://mlsanity.com/paper/2604.08412/) (2026)

## Abstract (arXiv)

In many speech-enabled human-machine interaction scenarios, user speech can overlap with the device playback audio. In these instances, the performance of tasks such as keyword-spotting (KWS) and device-directed speech detection (DDD) can degrade significantly. To address this problem, we propose an implicit acoustic echo cancellation (iAEC) framework where a neural network is trained to exploit the additional information from a reference microphone channel to learn to ignore the interfering signal and improve detection performance. We study this framework for the tasks of KWS and DDD on, respectively, an augmented version of Google Speech Commands v2 and a real-world Alexa device dataset. Notably, we show a 56% reduction in false-reject rate for the DDD task during device playback conditions. We also show comparable or superior performance over a strong end-to-end neural echo cancellation + KWS baseline for the KWS task with an order of magnitude less computational requirements.