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Found 10 papers, 1 papers with code
Supervised Adaptation of Sequence-to-Sequence Speech Recognition Systems using Batch-Weighting
This problem is even bigger for end-to-end speech recognition systems that only accept transcribed speech as training data, which is harder and more expensive to obtain than text data.
KIT’s IWSLT 2021 Offline Speech Translation System
We describe a system in both cascaded condition and end-to-end condition.
Continuously Learning New Words in Automatic Speech Recognition
Despite recent advances, Automatic Speech Recognition (ASR) systems are still far from perfect.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-End Evaluation for Low-Latency Simultaneous Speech Translation
Secondly, we compare different approaches to low-latency speech translation using this framework.
Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition
Code-Switching (CS) is referred to the phenomenon of alternately using words and phrases from different languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Code-Switching without Switching: Language Agnostic End-to-End Speech Translation
We propose a) a Language Agnostic end-to-end Speech Translation model (LAST), and b) a data augmentation strategy to increase code-switching (CS) performance.
Short-Term Word-Learning in a Dynamically Changing Environment
In this paper we study, a) methods to acquire important words for this memory dynamically and, b) the trade-off between improvement in recognition accuracy of new words and the potential danger of false alarms for those added words.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognition
To alleviate this problem we supplement an end-to-end ASR system with a word/phrase memory and a mechanism to access this memory to recognize the words and phrases correctly.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
VPNet: Variable Projection Networks
Based on these advantages and the promising results obtained, we anticipate a profound impact on the broader field of signal processing, in particular on classification, regression and clustering problems.
PolyLinkR: A linkage-sensitive gene set enrichment R package
We introduce PolyLinkR, an R package for gene set enrichment analysis that implements a novel null-model that accounts for linkage disequilibrium between genes belonging to the same gene set - a potential cause of false positives that is often not controlled for in similar tools.
