Fetching the paper…

Reading the bibliography…

2019

SANTLR: Speech Annotation Toolkit for Low Resource Languages

Li, Xinjian, Zhou, Zhong, Dalmia, Siddharth et al.

Original
PDF
Post on X

Understand

While low resource speech recognition has attracted a lot of attention from the speech community, there are a few tools available to facilitate low resource speech collection.

In this work, we present SANTLR: Speech Annotation Toolkit for Low Resource Languages.
It is a web-based toolkit which allows researchers to easily collect and annotate a corpus of speech in a low resource language.
Annotators may use this toolkit for two purposes: transcription or recording.

SANTLR: Speech Annotation Toolkit for Low Resource Languages · Around

Built on

T. Schultz, A. W. Black, S. Badaskar, M. Hornyak, and J. Kominek, “Spice: Web-based tools for rapid language adaptation in speech processing systems,” in
2007
Earlier work this paper cites.
M. P. Lewis,
2009
Earlier work this paper cites.

Similar

Y. Miao, M. Gowayyed, and F. Metze, “Eesen: End-to-end speech recognition using deep rnn models and wfst-based decoding,” in
Original
2015
Cited alongside, same era.
W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, and G. Zweig, “Achieving human parity in conversational speech recognition,”
2016
Cited alongside, same era.

Then

D. Amodei, S. Ananthanarayanan, R. Anubhai, J. Bai, E. Battenberg, C. Case, J. Casper, B. Catanzaro, Q. Cheng, G. Chen
2016
Later among the works it cites.
A. Chaudhary, S. Dalmia, J. Hu, X. Li, A. Matthews, A. O. Muis, N. Otani, S. Rijhwani, Z. Sheikh, N. Vyas
Original
2019
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…