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Related Experiment Video

Updated: Sep 29, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Two-stage streaming keyword detection and localization with multi-scale depthwise temporal convolution.

Jingyong Hou¹, Lei Xie¹, Shilei Zhang²

¹Audio, Speech and Language Processing Group (ASLP@NPU), ASGO, School of Computer Science, Northwestern Polytechnical University, Xi'an, China.

Neural Networks : the Official Journal of the International Neural Network Society

|March 18, 2022

Summary

This summary is machine-generated.

This study introduces an efficient two-stage keyword spotting system for smart devices. The novel method improves keyword detection and localization accuracy, outperforming existing approaches.

Keywords:

Keyword spotting Multi-scale Temporal convolution Two-stage Wake-up word detection and localization

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Speech processing and machine learning
Embedded systems and signal processing

Background:

Keyword spotting (KWS) systems on smart devices require high accuracy, efficiency, and a small footprint.
Existing methods often struggle with accurate keyword detection and precise location prediction in audio streams.

Purpose of the Study:

To propose a novel two-stage KWS method combining a multi-scale depthwise temporal convolution (MDTC) feature extractor and a two-stage detection/localization module.
To enhance the efficiency and accuracy of keyword spotting and localization on resource-constrained devices.

Main Methods:

Developed a Multi-Scale Depthwise Temporal Convolution (MDTC) feature extractor for efficient multi-scale feature representation.
Implemented a two-stage KWS approach using a Region Proposal Network (RPN) for initial keyword detection and localization.
Incorporated hard example mining to address class imbalance in RPN training and a second stage for refined classification and localization.

Main Results:

The MDTC feature extractor achieved a state-of-the-art command classification error rate of 1.74% on the Google Speech Command dataset.
The two-stage KWS method demonstrated a 27-32% better false rejection rate at one false alarm per hour on a commercial wake-up word (WuW) dataset.
Achieved a mean intersection-over-union ratio greater than 0.95 for keyword localization, significantly outperforming a one-stage RPN method.

Conclusions:

The proposed two-stage KWS method with the MDTC feature extractor offers significant improvements in both keyword detection and localization accuracy.
This approach is highly effective for wake-up word detection and localization tasks on smart devices, balancing performance with efficiency.
The novel MDTC feature extractor and two-stage architecture represent a substantial advancement in efficient and accurate keyword spotting.