Approaches to Multi-domain Language Recognition
By Mitchell McLaren, Mahesh Kumar Nandwana, Diego Castán, Luciana Ferrer. 2018
10 citations recorded by the source corpus.
Multi-domain language recognition involves the application of a language identification (LID) system to identify languages in more than one domain. This problem was the focus of the recent NIST LRE 2017, and this article presents the findings from the SRI team during sys-tem development for the evaluation. Approaches found to provide robustness in multi-domain LID include a domain-and-language-weighted Gaussian backend clas-sifier, duration-aware calibration, and a source normalized multi-resolution neural network backend. The recently developed speaker embeddings technology is also applied to the task of language recognition, showing great potential for future LID research.
Subfields: Artificial Intelligence, Natural Language Processing
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Citations in the corpus
- VOXLINGUA107: A Dataset for Spoken Language Recognition (2020, background): “The research group behind SITW also describe a similar in-house dataset for language recognition which contains 2180 audio files sourced from open-source videos and represents 29 languages [7], but it has never been publicly released.”