DE eng

Search in the Catalogues and Directories

Hits 1 – 10 of 10

1
The Multilingual TEDx Corpus for Speech Recognition and Translation ...
BASE
Show details
2
End-to-end ASR to jointly predict transcriptions and linguistic annotations ...
NAACL 2021 2021; Fujita, Yuya; Omachi, Motoi. - : Underline Science Inc., 2021
BASE
Show details
3
A Corpus for Large-Scale Phonetic Typology ...
BASE
Show details
4
A Corpus for Large-Scale Phonetic Typology ...
BASE
Show details
5
A corpus for large-scale phonetic typology
BASE
Show details
6
A Corpus for Large-Scale Phonetic Typology
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
BASE
Show details
7
Massively Multilingual Adversarial Speech Recognition ...
BASE
Show details
8
Analysis of Multilingual Sequence-to-Sequence speech recognition systems ...
BASE
Show details
9
Low-Resource Contextual Topic Identification on Speech ...
BASE
Show details
10
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling ...
Abstract: Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new direction in speech research. The approach benefits by performing model training without using lexicon and alignments. However, this poses a new problem of requiring more data compared to conventional DNN-HMM systems. In this work, we attempt to use data from 10 BABEL languages to build a multi-lingual seq2seq model as a prior model, and then port them towards 4 other BABEL languages using transfer learning approach. We also explore different architectures for improving the prior multilingual seq2seq model. The paper also discusses the effect of integrating a recurrent neural network language model (RNNLM) with a seq2seq model during decoding. Experimental results show that the transfer learning approach from the multilingual model shows substantial gains over monolingual models across all 4 BABEL languages. Incorporating an RNNLM also brings significant improvements in terms of %WER, and achieves recognition performance ...
Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD
URL: https://arxiv.org/abs/1810.03459
https://dx.doi.org/10.48550/arxiv.1810.03459
BASE
Hide details

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
10
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern