Multi-layer Attention Mechanism for Speech Keyword Recognition

by   Ruisen Luo, et al.

As an important part of speech recognition technology, automatic speech keyword recognition has been intensively studied in recent years. Such technology becomes especially pivotal under situations with limited infrastructures and computational resources, such as voice command recognition in vehicles and robot interaction. At present, the mainstream methods in automatic speech keyword recognition are based on long short-term memory (LSTM) networks with attention mechanism. However, due to inevitable information losses for the LSTM layer caused during feature extraction, the calculated attention weights are biased. In this paper, a novel approach, namely Multi-layer Attention Mechanism, is proposed to handle the inaccurate attention weights problem. The key idea is that, in addition to the conventional attention mechanism, information of layers prior to feature extraction and LSTM are introduced into attention weights calculations. Therefore, the attention weights are more accurate because the overall model can have more precise and focused areas. We conduct a comprehensive comparison and analysis on the keyword spotting performances on convolution neural network, bi-directional LSTM cyclic neural network, and cyclic neural network with the proposed attention mechanism on Google Speech Command datasets V2 datasets. Experimental results indicate favorable results for the proposed method and demonstrate the validity of the proposed method. The proposed multi-layer attention methods can be useful for other researches related to object spotting.


page 1

page 5


Explaining the Attention Mechanism of End-to-End Speech Recognition Using Decision Trees

The attention mechanism has largely improved the performance of end-to-e...

A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction

Earthquakes, as natural phenomena, have continuously caused damage and l...

Noise Classification Aided Attention-Based Neural Network for Monaural Speech Enhancement

This paper proposes an noise type classification aided attention-based n...

Multi-task Learning with Cross Attention for Keyword Spotting

Keyword spotting (KWS) is an important technique for speech applications...

ConvMath: A Convolutional Sequence Network for Mathematical Expression Recognition

Despite the recent advances in optical character recognition (OCR), math...

A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling

As the first robotic platforms slowly approach our everyday life, we can...