Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Kate Knill"'
Publikováno v:
Interspeech 2022.
Autor:
Chee Wee Leong, Kate Knill, Daniele Falavigna, A. Misra, Marco Matassoni, L. Wang, Roberto Gretter
Publikováno v:
Interspeech 2021.
Publikováno v:
ICASSP
A significant concern with deep learning based approaches is that they are difficult to interpret, which means detecting bias in network predictions can be challenging. Concept Activation Vectors (CAVs) have been proposed to address this problem. The
Autor:
M Rashid, Mark J. F. Gales, R. C. van Dalen, Kate Knill, Andrey Malinin, Yu Wang, Konstantinos G. Kyriakopoulos
Publikováno v:
Speech Communication. 104:47-56
With increasing global demand for learning English as a second language, there has been considerable interest in methods of automatic assessment of spoken language proficiency for use in interactive electronic learning tools as well as for grading ca
Publikováno v:
INTERSPEECH
Deep learning has dramatically improved the performance of automated systems on a range of tasks including spoken language assessment. One of the issues with these deep learning approaches is that they tend to be overconfident in the decisions that t
Publikováno v:
INTERSPEECH
There is an increasing demand for automated spoken language assessment (SLA) systems, partly driven by the performance improvements that have come from deep learning based approaches. One aspect of deep learning systems is that they do not require ex
Publikováno v:
INTERSPEECH
Publikováno v:
INTERSPEECH
Publikováno v:
BEA@ACL
Increased demand to learn English for business and education has led to growing interest in automatic spoken language assessment and teaching systems. With this shift to automated approaches it is important that systems reliably assess all aspects of
Publikováno v:
COLING
The 28th International Conference on Computational Linguistics (COLING 2020)
The 28th International Conference on Computational Linguistics (COLING 2020)
We describe the collection of transcription corrections and grammatical error annotations for the CrowdED Corpus of spoken English monologues on business topics. The corpus recordings were crowdsourced from native speakers of English and learners of