Search Results for author:
Found 8 papers, 1 papers with code
Pre-training on high-resource speech recognition improves low-resource speech-to-text translation
Finally, we show that the approach improves performance on a true low-resource task: pre-training on a combination of English ASR and French ASR improves Mboshi-French ST, where only 4 hours of data are available, from 3. 5 to 7. 1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Low-Resource Speech-to-Text Translation
We explore models trained on between 20 and 160 hours of data, and find that although models trained on less data have considerably lower BLEU scores, they can still predict words with relatively high precision and recall---around 50% for a model trained on 50 hours of data, versus around 60% for the full 160 hour model.
Towards speech-to-text translation without speech recognition
We explore the problem of translating speech to text in low-resource scenarios where neither automatic speech recognition (ASR) nor machine translation (MT) are available, but we have training data in the form of audio paired with text translations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Weakly supervised spoken term discovery using cross-lingual side information
Recent work on unsupervised term discovery (UTD) aims to identify and cluster repeated word-like units from audio alone.
Spoken Term Discovery for Language Documentation using Translations
Vast amounts of speech data collected for language documentation and research remain untranscribed and unsearchable, but often a small amount of speech may have text translations available.
Cross-lingual topic prediction for speech using translations
Given a large amount of unannotated speech in a low-resource language, can we classify the speech utterances by topic?
Analyzing ASR pretraining for low-resource speech-to-text translation
Previous work has shown that for low-resource source languages, automatic speech-to-text translation (AST) can be improved by pretraining an end-to-end model on automatic speech recognition (ASR) data from a high-resource language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Comparing Euclidean and Hyperbolic Embeddings on the WordNet Nouns Hypernymy Graph
Nickel and Kiela (2017) present a new method for embedding tree nodes in the Poincare ball, and suggest that these hyperbolic embeddings are far more effective than Euclidean embeddings at embedding nodes in large, hierarchically structured graphs like the WordNet nouns hypernymy tree.
