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pro vyhledávání: '"Bhandari, Nishchal"'
Autor:
Bhandari, Nishchal, Chen, Danny, Fernández, Miguel Ángel del Río, Delworth, Natalie, Fox, Jennifer Drexler, Jetté, Migüel, McNamara, Quinten, Miller, Corey, Novotný, Ondřej, Profant, Ján, Qin, Nan, Ratajczak, Martin, Robichaud, Jean-Philippe
Today, we are open-sourcing our core speech recognition and diarization models for non-commercial use. We are releasing both a full production pipeline for developers as well as pared-down research models for experimentation. Rev hopes that these rel
Externí odkaz:
http://arxiv.org/abs/2410.03930
Autor:
Heuser, Annika, Kendall, Tyler, del Rio, Miguel, McNamara, Quinten, Bhandari, Nishchal, Miller, Corey, Jetté, Migüel
Common measures of accuracy used to assess the performance of automatic speech recognition (ASR) systems, as well as human transcribers, conflate multiple sources of error. Stylistic differences, such as verbatim vs non-verbatim, can play a significa
Externí odkaz:
http://arxiv.org/abs/2409.03059
Autor:
Del Rio, Miguel, Delworth, Natalie, Westerman, Ryan, Huang, Michelle, Bhandari, Nishchal, Palakapilly, Joseph, McNamara, Quinten, Dong, Joshua, Zelasko, Piotr, Jette, Miguel
Commonly used speech corpora inadequately challenge academic and commercial ASR systems. In particular, speech corpora lack metadata needed for detailed analysis and WER measurement. In response, we present Earnings-21, a 39-hour corpus of earnings c
Externí odkaz:
http://arxiv.org/abs/2104.11348
Autor:
Hinsvark, Arthur, Delworth, Natalie, Del Rio, Miguel, McNamara, Quinten, Dong, Joshua, Westerman, Ryan, Huang, Michelle, Palakapilly, Joseph, Drexler, Jennifer, Pirkin, Ilya, Bhandari, Nishchal, Jette, Miguel
Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The phonetic and linguistic variability of accents present hard challenges for ASR systems today in both data collection and modeling strategies. The resulting bias in A
Externí odkaz:
http://arxiv.org/abs/2104.10747
Autor:
Madan, Spandan, Henry, Timothy, Dozier, Jamell, Ho, Helen, Bhandari, Nishchal, Sasaki, Tomotake, Durand, Frédo, Pfister, Hanspeter, Boix, Xavier
Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent works suggest that convolutional neural networks (CNNs) fail to generalize to out-of-distribution (OOD) category-viewpoint combinations, ie. combinations not
Externí odkaz:
http://arxiv.org/abs/2007.08032
Autor:
Bhandari, Nishchal
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and
Externí odkaz:
http://hdl.handle.net/1721.1/119745
Akademický článek
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Publikováno v:
2015 IEEE/RSJ International Conference on Intelligent Robots & Systems (IROS); 2015, p1913-1920, 8p
Publikováno v:
MIT Web Domain
IROS
IROS
Drift of the rotation estimate is a well known problem in visual odometry systems as it is the main source of positioning inaccuracy. We propose three novel algorithms to estimate the full 3D rotation to the surrounding Manhattan World (MW) in as sho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e55b3ed59e12803d4c3f49fbc755a303
http://hdl.handle.net/1721.1/107428
http://hdl.handle.net/1721.1/107428