DE eng

Search in the Catalogues and Directories

Hits 1 – 18 of 18

1
Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation ...
BASE
Show details
2
Representing `how you say' with `what you say': English corpus of focused speech and text reflecting corresponding implications ...
Suzuki, Naoaki; Nakamura, Satoshi. - : arXiv, 2022
BASE
Show details
3
Applying Syntax$\unicode{x2013}$Prosody Mapping Hypothesis and Prosodic Well-Formedness Constraints to Neural Sequence-to-Sequence Speech Synthesis ...
BASE
Show details
4
Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation ...
BASE
Show details
5
Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation ...
BASE
Show details
6
Simultaneous Neural Machine Translation with Constituent Label Prediction ...
BASE
Show details
7
Transformer VQ-VAE for Unsupervised Unit Discovery and Speech Synthesis: ZeroSpeech 2020 Challenge ...
BASE
Show details
8
Speech-to-speech Translation between Untranscribed Unknown Languages ...
BASE
Show details
9
Towards Machine Speech-to-speech Translation
BASE
Show details
10
Multi-Source Neural Machine Translation with Missing Data ...
BASE
Show details
11
Local Monotonic Attention Mechanism for End-to-End Speech and Language Processing ...
BASE
Show details
12
Listening while Speaking: Speech Chain by Deep Learning ...
BASE
Show details
13
Incorporating Discrete Translation Lexicons into Neural Machine Translation ...
Abstract: Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understanding the meaning of the sentence. We propose a method to alleviate this problem by augmenting NMT systems with discrete translation lexicons that efficiently encode translations of these low-frequency words. We describe a method to calculate the lexicon probability of the next word in the translation candidate by using the attention vector of the NMT model to select which source word lexical probabilities the model should focus on. We test two methods to combine this probability with the standard NMT probability: (1) using it as a bias, and (2) linear interpolation. Experiments on two corpora show an improvement of 2.0-2.3 BLEU and 0.13-0.44 NIST score, and faster convergence time. ... : Accepted at EMNLP 2016 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1606.02006
https://arxiv.org/abs/1606.02006
BASE
Hide details
14
Context Awareness and Priority Control for ITS based on Automatic Speech Recognition
In: International conference on ITS Telecommunications ; https://hal.inria.fr/hal-01225312 ; International conference on ITS Telecommunications, Dec 2015, Copenhagen, Denmark ; http://www.itst-conf.org/ (2015)
BASE
Show details
15
Neural Reranking Improves Subjective Quality of Machine Translation: NAIST at WAT2015 ...
BASE
Show details
16
Spoken Dialogue Systems for Ambient Environments : Second International Workshop, IWSDS 2010, Gotemba, Shizuoka, Japan, October 1-2, 2010. Proceedings
Lee, Gary Geunbae; Mariani, Joseph; Minker, Wolfgang. - Berlin, Heidelberg : Springer Berlin Heidelberg, 2010
UB Frankfurt Linguistik
Show details
17
Cultural communication idiosyncrasies in human-computer interaction
Miehle, Juliana; Ultes, Stefan; Minker, Wolfgang. - : ACL (Association for Computational Linguistics)
BASE
Show details
18ALAGIN - Advanced LAnGuage INformation Forum
http://www.alagin.jp/
Topic: Computational linguistics; Corpus linguistics; Pragmalinguistics / Communication research; ...
Language: Chinese, Mandarin; English; Japanese
Source type: Corpora; Linguistic associations; Software / Tools
Access: free access

Catalogues
1
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
1
0
0
0
Open access documents
16
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern