Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Snover, Matthew"'
Cross-lingual conversational speech summarization is an important problem, but suffers from a dearth of resources. While transcriptions exist for a number of languages, translated conversational speech is rare and datasets containing summaries are no
Externí odkaz:
http://arxiv.org/abs/2408.06484
Transcribing the speech of multiple overlapping speakers typically requires separating the audio into multiple streams and recognizing each one independently. More recent work jointly separates and transcribes, but requires a separate decoding compon
Externí odkaz:
http://arxiv.org/abs/2408.06474
This paper introduces a set of English translations for a 123-hour subset of the CallHome Mandarin Chinese data and the HKUST Mandarin Telephone Speech data for the task of speech translation. Paired source-language speech and target-language text is
Externí odkaz:
http://arxiv.org/abs/2404.11619
Advances in self-supervised learning have significantly reduced the amount of transcribed audio required for training. However, the majority of work in this area is focused on read speech. We explore limited supervision in the domain of conversationa
Externí odkaz:
http://arxiv.org/abs/2210.15135
Recent advances in unsupervised representation learning have demonstrated the impact of pretraining on large amounts of read speech. We adapt these techniques for domain adaptation in low-resource -- both in terms of data and compute -- conversationa
Externí odkaz:
http://arxiv.org/abs/2110.15836
Sequence-to-sequence (seq2seq) models are competitive with hybrid models for automatic speech recognition (ASR) tasks when large amounts of training data are available. However, data sparsity and domain adaptation are more problematic for seq2seq mod
Externí odkaz:
http://arxiv.org/abs/2106.07716
Modeling code-switched speech is an important problem in automatic speech recognition (ASR). Labeled code-switched data are rare, so monolingual data are often used to model code-switched speech. These monolingual data may be more closely matched to
Externí odkaz:
http://arxiv.org/abs/2106.07699
Publikováno v:
Machine Translation, 2009 Sep 01. 23(2/3), 169-179.
Externí odkaz:
https://www.jstor.org/stable/40783467
Publikováno v:
Machine Translation, 2009 Sep 01. 23(2/3), 117-127.
Externí odkaz:
https://www.jstor.org/stable/40783463
Publikováno v:
2016 IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP); 2016, p2244-2248, 5p