Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Ozlem Kalinli"'
Autor:
Alexander Bertrand, Ozlem Kalinli
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 5, Pp 630-631 (2024)
The 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023) took place in Rhodos, Greece, running from June 4th to June 10th, with a record number of paper submissions and attendees. Since 2021, ICASSP has feature
Externí odkaz:
https://doaj.org/article/b888793bba1c44f6b85bb69b994cffb2
Autor:
Andros Tjandra, Nayan Singhal, David Zhang, Ozlem Kalinli, Abdelrahman Mohamed, Duc Le, Michael L. Seltzer
End-to-end multilingual ASR has become more appealing because of several reasons such as simplifying the training and deployment process and positive performance transfer from high-resource to low-resource languages. However, scaling up the number of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f736edcbf4036a4038a642fa6cb5d292
http://arxiv.org/abs/2211.05756
http://arxiv.org/abs/2211.05756
Autor:
Jay Mahadeokar, Yangyang Shi, Ke Li, Duc Le, Jiedan Zhu, Vikas Chandra, Ozlem Kalinli, Michael Seltzer
Publikováno v:
Interspeech 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6a8cb9411a465c8c569fcf5c341a3ef
https://doi.org/10.21437/interspeech.2022-10551
https://doi.org/10.21437/interspeech.2022-10551
Autor:
Antoine Bruguier, Duc Le, Rohit Prabhavalkar, Dangna Li, Zhe Liu, Bo Wang, Eun Chang, Fuchun Peng, Ozlem Kalinli, Michael L. Seltzer
We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transducers (FSTs) in a mathematically consistent framework. Our method utilize
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c5040630a954cf9a93faa45dc9282ccd
Autor:
Duc Le, Akshat Shrivastava, Paden D. Tomasello, Suyoun Kim, Aleksandr Livshits, Ozlem Kalinli, Michael Seltzer
We propose a novel deliberation-based approach to end-to-end (E2E) spoken language understanding (SLU), where a streaming automatic speech recognition (ASR) model produces the first-pass hypothesis and a second-pass natural language understanding (NL
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3447f2c0d0ee554927d59429ad7fc5ba
Autor:
Yangyang Shi, Ozlem Kalinli, Ganesh Venkatesh, Varun K. Nagaraja, Vikas Chandra, Michael L. Seltzer
Publikováno v:
Interspeech 2021.
On-device speech recognition requires training models of different sizes for deploying on devices with various computational budgets. When building such different models, we can benefit from training them jointly to take advantage of the knowledge sh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37b8336b74565b760be37fc3fb7f2aa2
https://doi.org/10.21437/interspeech.2021-354
https://doi.org/10.21437/interspeech.2021-354
Autor:
Christian Fuegen, Abhinav Arora, Michael L. Seltzer, Ching-Feng Yeh, Suyoun Kim, Ozlem Kalinli, Duc Le
Publikováno v:
Interspeech 2021.
Word Error Rate (WER) has been the predominant metric used to evaluate the performance of automatic speech recognition (ASR) systems. However, WER is sometimes not a good indicator for downstream Natural Language Understanding (NLU) tasks, such as in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::594941bd38cbd21fb7c332aef922cf3c
https://doi.org/10.21437/interspeech.2021-1929
https://doi.org/10.21437/interspeech.2021-1929
Autor:
Jay Mahadeokar, Alex Xiao, Christian Fuegen, Duc Le, Michael L. Seltzer, Yuan Shangguan, Chunyang Wu, Hang Su, Ozlem Kalinli, Yangyang Shi
Publikováno v:
Interspeech 2021.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a7cf41c731f314441418274c830f0a3
https://doi.org/10.21437/interspeech.2021-1921
https://doi.org/10.21437/interspeech.2021-1921
Autor:
Christian Fuegen, Ozlem Kalinli, Chunyang Wu, Zhiping Xiu, Thilo Koehler, Qing He, Yangyang Shi
Publikováno v:
Interspeech 2021.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33ab86092f8673aae38d03838eed1f0e
https://doi.org/10.21437/interspeech.2021-1655
https://doi.org/10.21437/interspeech.2021-1655
Autor:
Ching-Feng Yeh, Ozlem Kalinli, Yangyang Shi, Chunyang Wu, Rohit Prabhavalkar, Alex Xiao, Christian Fuegen, Duc Le, Michael L. Seltzer, Varun K. Nagaraja, Julian Chan, Jay Mahadeokar
We propose a dynamic encoder transducer (DET) for on-device speech recognition. One DET model scales to multiple devices with different computation capacities without retraining or finetuning. To trading off accuracy and latency, DET assigns differen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c19b870d3c9858d0288619e077dd8bb4
http://arxiv.org/abs/2104.02176
http://arxiv.org/abs/2104.02176