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

09/12/2017
by   Zhiming Wang, et al.
0

Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount. DNN is to directly predict the posterior of phoneme units of any personally customized key-phrase, and CTC to produce a confidence score of the given phoneme sequence as responsive decision-making mechanism. The CTC-KWS has competitive performance in comparison with purely DNN based keyword specific KWS, but not increasing any computational complexity.

READ FULL TEXT
research
12/06/2018

End-to-End Streaming Keyword Spotting

We present a system for keyword spotting that, except for a frontend com...
research
12/10/2017

A Cascade Architecture for Keyword Spotting on Mobile Devices

We present a cascade architecture for keyword spotting with speaker veri...
research
09/01/2021

A Separable Temporal Convolution Neural Network with Attention for Small-Footprint Keyword Spotting

Keyword spotting (KWS) on mobile devices generally requires a small memo...
research
05/30/2020

Exploring Filterbank Learning for Keyword Spotting

Despite their great performance over the years, handcrafted speech featu...
research
10/26/2022

HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words

Streaming keyword spotting is a widely used solution for activating voic...
research
10/31/2018

A Mixture of Expert Approach for Low-Cost Customization of Deep Neural Networks

The ability to customize a trained Deep Neural Network (DNN) locally usi...
research
11/06/2018

Hierarchical Neural Network Architecture In Keyword Spotting

Keyword Spotting (KWS) provides the start signal of ASR problem, and thu...

Please sign up or login with your details

Forgot password? Click here to reset