Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

04/09/2018
by   Pete Warden, et al.
0

Describes an audio dataset of spoken words designed to help train and evaluate keyword spotting systems. Discusses why this task is an interesting challenge, and why it requires a specialized dataset that is different from conventional datasets used for automatic speech recognition of full sentences. Suggests a methodology for reproducible and comparable accuracy metrics for this task. Describes how the data was collected and verified, what it contains, previous versions and properties. Concludes by reporting baseline results of models trained on this dataset.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/29/2019

Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset

We present an experimental dataset, Basic Dataset for Sorani Kurdish Aut...
research
10/14/2022

Bringing NURC/SP to Digital Life: the Role of Open-source Automatic Speech Recognition Models

The NURC Project that started in 1969 to study the cultured linguistic u...
research
01/09/2020

Open Challenge for Correcting Errors of Speech Recognition Systems

The paper announces the new long-term challenge for improving the perfor...
research
07/04/2014

Recognition of Isolated Words using Zernike and MFCC features for Audio Visual Speech Recognition

Automatic Speech Recognition (ASR) by machine is an attractive research ...
research
03/15/2023

A large-scale multimodal dataset of human speech recognition

Nowadays, non-privacy small-scale motion detection has attracted an incr...
research
08/10/2020

Subword Regularization: An Analysis of Scalability and Generalization for End-to-End Automatic Speech Recognition

Subwords are the most widely used output units in end-to-end speech reco...
research
01/10/2013

Statistical Modeling in Continuous Speech Recognition (CSR)(Invited Talk)

Automatic continuous speech recognition (CSR) is sufficiently mature tha...

Please sign up or login with your details

Forgot password? Click here to reset