End-to-end Keyword Spotting using Xception-1d

10/09/2021
by   Iván Vallés-Pérez, et al.
0

The field of conversational agents is growing fast and there is an increasing need for algorithms that enhance natural interaction. In this work we show how we achieved state of the art results in the Keyword Spotting field by adapting and tweaking the Xception algorithm, which achieved outstanding results in several computer vision tasks. We obtained about 96% accuracy when classifying audio clips belonging to 35 different categories, beating human annotation at the most complex tasks proposed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/13/2021

Keyword Extraction for Improved Document Retrieval in Conversational Search

Recent research has shown that mixed-initiative conversational search, b...
research
09/21/2021

Audiomer: A Convolutional Transformer for Keyword Spotting

Transformers have seen an unprecedented rise in Natural Language Process...
research
12/06/2018

End-to-End Streaming Keyword Spotting

We present a system for keyword spotting that, except for a frontend com...
research
12/15/2020

Keyword-Guided Neural Conversational Model

We study the problem of imposing conversational goals/keywords on open-d...
research
06/08/2023

Matching Latent Encoding for Audio-Text based Keyword Spotting

Using audio and text embeddings jointly for Keyword Spotting (KWS) has s...
research
10/18/2021

VRM-Phase I VKW system description of long-short video customizable keyword wakeup challenge

Keyword wakeup technology has always been a research hotspot in speech p...
research
07/08/2022

A Multi-tasking Model of Speaker-Keyword Classification for Keeping Human in the Loop of Drone-assisted Inspection

Audio commands are a preferred communication medium to keep inspectors i...

Please sign up or login with your details

Forgot password? Click here to reset