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Found 6 papers, 1 papers with code
The IWSLT 2019 KIT Speech Translation System
This paper describes KIT’s submission to the IWSLT 2019 Speech Translation task on two sub-tasks corresponding to two different datasets.
German-Arabic Speech-to-Speech Translation for Psychiatric Diagnosis
As this is a very specific domain, in addition to the linguistic challenges posed by translating between Arabic and German, we also focus in this paper on the methods we implemented for adapting our speech translation system to the domain of this psychiatric interview.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
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.
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
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
DaCToR: A Data Collection Tool for the RELATER Project
Collecting domain-specific data for under-resourced languages, e. g., dialects of languages, can be very expensive, potentially financially prohibitive and taking long time.
