Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Mohamed G. Elfeky"'
Publikováno v:
Procedia Computer Science. 128:1-8
Research has shown that automatic speech recognition (ASR) performance typically decreases when evaluated on a dialectal variation of the same language that was not used for training its models. Similarly, models simultaneously trained on a group of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4908bbb9a3cc1ec1a7b922b32c5264bf
https://doi.org/10.1016/j.procs.2018.03.001
https://doi.org/10.1016/j.procs.2018.03.001
Autor:
Khe Chai Sim, Mohamed G. Elfeky, Trevor Strohman, Ananya Misra, Michiel Bacchiani, Arun Narayanan, Anshuman Tripathi, Golan Pundak, Parisa Haghani
Publikováno v:
SLT
Current state-of-the-art automatic speech recognition systems are trained to work in specific `domains', defined based on factors like application, sampling rate and codec. When such recognizers are used in conditions that do not match the training d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6f8761113c076ea769bce14469f25b5
https://doi.org/10.1109/slt.2018.8639610
https://doi.org/10.1109/slt.2018.8639610
Publikováno v:
SLT
Acoustic model performance typically decreases when evaluated on a dialectal variation of the same language that was not used during training. Similarly, models simultaneously trained on a group of dialects tend to underperform dialect-specific model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74546fca07da38c8e489c955fce5e83a
https://doi.org/10.1109/slt.2016.7846328
https://doi.org/10.1109/slt.2016.7846328
Publikováno v:
ICASSP
While research has often shown that building dialect-specific Automatic Speech Recognizers is the optimal approach to dealing with dialectal variations of the same language, we have observed that dialect-specific recognizers do not always output the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50508778d24179c0bb2a2dec3592ca90
https://doi.org/10.1109/icassp.2016.7472798
https://doi.org/10.1109/icassp.2016.7472798
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 16:335-345
Mining of periodic patterns in time-series databases is an interesting data mining problem. It can be envisioned as a tool for forecasting and prediction of the future behavior of time-series data. Incremental mining refers to the issue of maintainin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::38b1e62763b19e5cd9f84e721f3793c3
https://doi.org/10.1109/tkde.2003.1262186
https://doi.org/10.1109/tkde.2003.1262186
Publikováno v:
The VLDB Journal The International Journal on Very Large Data Bases. 12:28-40
In an error-free system with perfectly clean data, the construction of a global view of the data consists of linking - in relational terms, joining - two or more tables on their key fields. Unfortunately, most of the time, these data are neither care
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1849dc097931f6ddc3271d70f4cf2200
https://doi.org/10.1007/s00778-002-0072-y
https://doi.org/10.1007/s00778-002-0072-y
Publikováno v:
ICDM
Department of Computer Science Technical Reports
Department of Computer Science Technical Reports
Sensor devices are becoming ubiquitous, especially in measurement and monitoring applications. Because of the real-time, append-only and semi-infinite natures of the generated sensor data streams, an online incremental approach is a necessity for min
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::380b4f2c5674da9a90baf6648f98517f
https://doi.org/10.1109/icdm.2006.153
https://doi.org/10.1109/icdm.2006.153
Publikováno v:
ICDM
Periodicity mining is used for predicting trends in time series data. Periodicity detection is an essential process in periodicity mining to discover potential periodicity rates. Existing periodicity detection algorithms do not take into account the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e0d3170da5250d325a2c8db4f6a25f11
https://doi.org/10.1109/icdm.2005.152
https://doi.org/10.1109/icdm.2005.152
Autor:
Mohamed G. Elfeky, Thanaa M. Ghanem, R. Gwadera, Mohamed Y. Eltabakh, Ihab F. Ilyas, Mohamed Ali, Mirette Marzouk, Ahmed K. Elmagarmid, Walid G. Aref, Moustafa A. Hammad, Mohamed F. Mokbel, Ann Christine Catlin, Xiaopeng Xiong
Publikováno v:
ICDE
We present the demonstration of the design of "STEAM", Purdue Boiler Makers' stream database system that allows for the processing of continuous and snap-shot queries over data streams. Specifically, the demonstration focuses on the query processing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c4127caa9f35dd9d2650ad22c0a4b80
https://doi.org/10.1109/icde.2004.1320080
https://doi.org/10.1109/icde.2004.1320080
Publikováno v:
Advances in Database Technology-EDBT 2004 ISBN: 9783540212003
EDBT
EDBT
The mining of periodic patterns in time series databases is an interesting data mining problem that can be envisioned as a tool for forecasting and predicting the future behavior of time series data. Existing periodic patterns mining algorithms eithe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c3cbbb0d53bd7257e0ce218da346f0bf
https://doi.org/10.1007/978-3-540-24741-8_35
https://doi.org/10.1007/978-3-540-24741-8_35
