Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Prashant Serai"'
Autor:
Ruchika Verma, Hamed Akbari, Ramon Correa, Siddhesh Thakur, Virginia Hill, Michel Bilello, Sarthak Pati, Chiharu Sako, Russell T. Shinohara, Spyridon Bakas, Prashant Serai, Ludovic Venet, Geri Blake, Niha Beig, Sung Min Ha, Pallavi Tiwari
Publikováno v:
Med Phys
PURPOSE: The availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4795bee9aa4cf9de0896350e53a24d5a
https://doi.org/10.1002/mp.14556
https://doi.org/10.1002/mp.14556
Automatic Speech Recognition (ASR) is an imperfect process that results in certain mismatches in ASR output text when compared to plain written text or transcriptions. When plain text data is to be used to train systems for spoken language understand
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b4d531b1891ef03d3c98efe4ab0eb1a
Publikováno v:
ICASSP
Simulating the errors made by a speech recognizer on plain text has proven useful to help train downstream NLP tasks to be robust to real ASR errors at test time. Prior work in this domain has focused on modeling confusions at the phonetic level, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cf4283a227b022e2ce1875a63f85098a
https://doi.org/10.1109/icassp40776.2020.9054398
https://doi.org/10.1109/icassp40776.2020.9054398
Publikováno v:
ICASSP
Modeling the errors of a speech recognizer can help simulate errorful recognized speech data from plain text, which has proven useful for tasks like discriminative language modeling, improving robustness of NLP systems, where limited or even no audio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfad609f872188f94a1c5349cb7afdbe
https://doi.org/10.1109/icassp.2019.8682319
https://doi.org/10.1109/icassp.2019.8682319
Publikováno v:
ICASSP
Off-the-shelf speech recognition systems can yield useful results and accelerate application development, but general-purpose systems applied to specialized domains can introduce acoustically small—but semantically catastrophic—errors. Furthermor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::020ca6bfccbf54a132f130d62fa53381
https://doi.org/10.1109/icassp.2019.8682550
https://doi.org/10.1109/icassp.2019.8682550