Hierarchical Neural Network Architecture In Keyword Spotting

11/06/2018
by   Yixiao Qu, et al.
0

Keyword Spotting (KWS) provides the start signal of ASR problem, and thus it is essential to ensure a high recall rate. However, its real-time property requires low computation complexity. This contradiction inspires people to find a suitable model which is small enough to perform well in multi environments. To deal with this contradiction, we implement the Hierarchical Neural Network(HNN), which is proved to be effective in many speech recognition problems. HNN outperforms traditional DNN and CNN even though its model size and computation complexity are slightly less. Also, its simple topology structure makes easy to deploy on any device.

READ FULL TEXT
research
07/02/2018

weight-importance sparse training in keyword spotting

Large size models are implemented in recently ASR system to deal with co...
research
05/24/2022

Boosting Tail Neural Network for Realtime Custom Keyword Spotting

In this paper, we propose a Boosting Tail Neural Network (BTNN) for impr...
research
11/20/2017

Hello Edge: Keyword Spotting on Microcontrollers

Keyword spotting (KWS) is a critical component for enabling speech based...
research
12/06/2018

End-to-End Streaming Keyword Spotting

We present a system for keyword spotting that, except for a frontend com...
research
09/12/2017

Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier

Mainly for the sake of solving the lack of keyword-specific data, we pro...
research
08/17/2020

WSRNet: Joint Spotting and Recognition of Handwritten Words

In this work, we present a unified model that can handle both Keyword Sp...
research
07/21/2020

Very Fast Keyword Spotting System with Real Time Factor below 0.01

In the paper we present an architecture of a keyword spotting (KWS) syst...

Please sign up or login with your details

Forgot password? Click here to reset