Spoken Language Recognition with Cluster-Based Modeling-Reference-Cited by-同舟云学术

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Spoken Language Recognition with Cluster-Based Modeling

Published:2022-05-23 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Kacprzak Stanislaw¹,Rybicka Magdalena¹,Kowalczyk Konrad¹

Affiliation:

1. Electronics and Telecommunications AGH University of Science and Technology,Faculty of Computer Science,Krakow,Poland,30-059

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747515.pdf?arnumber=9747515

Reference41 articles.

1. LIBLINEAR: A library for large linear classification;fan;Journal of Machine Learning Research,2008

2. Focal multiclass toolkit;brummer,2014

3. AP17-OLR challenge: Data, plan, and baseline

4. Ap16-ol7: A multi-lingual database for oriental languages and a language recognition baseline;wang;2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA),2016

5. The Kaldi speech recognition toolkit;povey;2011 IEEE Workshop on Automatic Speech Recognition & Understanding,2011

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1. Self-Supervised Learning Representations for Dialect Identification with Sparse Transformers;2024 8th International Conference on Control Engineering and Artificial Intelligence;2024-01-26

2. Common latent representation learning for low-resourced spoken language identification;Multimedia Tools and Applications;2023-09-26

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