# Streaming ResLSTM with Causal Mean Aggregation for Device-Directed Utterance Detection

arXiv: https://arxiv.org/abs/2007.09245 · HTML: https://mlsanity.com/paper/2007.09245/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

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

- [Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation](https://mlsanity.com/paper/2105.06598/) (2021)
- [Streaming on-device detection of device directed speech from voice and touch-based invocation](https://mlsanity.com/paper/2110.04656/) (2021)

## Abstract (arXiv)

In this paper, we propose a streaming model to distinguish voice queries intended for a smart-home device from background speech. The proposed model consists of multiple CNN layers with residual connections, followed by a stacked LSTM architecture. The streaming capability is achieved by using unidirectional LSTM layers and a causal mean aggregation layer to form the final utterance-level prediction up to the current frame. In order to avoid redundant computation during online streaming inference, we use a caching mechanism for every convolution operation. Experimental results on a device-directed vs. non device-directed task show that the proposed model yields an equal error rate reduction of 41% compared to our previous best model on this task. Furthermore, we show that the proposed model is able to accurately predict earlier in time compared to the attention-based models.