Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Izhak Shafran"'
Publikováno v:
Interspeech 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc39ea552c17a4fcb78e3776640b3e37
https://doi.org/10.21437/interspeech.2022-391
https://doi.org/10.21437/interspeech.2022-391
Confidence estimate is an often requested feature in applications such as medical transcription where errors can impact patient care and the confidence estimate could be used to alert medical professionals to verify potential errors in recognition. I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ee11b588caa784e3ce6e45a4c418a98
http://arxiv.org/abs/2110.15222
http://arxiv.org/abs/2110.15222
Publikováno v:
Interspeech 2021.
In this paper, we describe novel components for extracting clinically relevant information from medical conversations which will be available as Google APIs. We describe a transformer-based Recurrent Neural Network Transducer (RNN-T) model tailored f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::703359cad183b3cf452aeaee2c49554b
https://doi.org/10.21437/interspeech.2021-691
https://doi.org/10.21437/interspeech.2021-691
Publikováno v:
SLT Workshop Spok Lang Technol
SLT
SLT
We investigate methods for detecting voiced segments in everyday conversations from ambient recordings. Such recordings contain high diversity of background noise, making it difficult or infeasible to collect representative labelled samples for estim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c2d47481bafdd21fd2b51b5f7cc9994
https://pubmed.ncbi.nlm.nih.gov/33644784
https://pubmed.ncbi.nlm.nih.gov/33644784
Publikováno v:
Proc IEEE Int Conf Acoust Speech Signal Process
ICASSP
ICASSP
Methods are proposed for measuring affective valence and arousal in speech. The methods apply support vector regression to prosodic and text features to predict human valence and arousal ratings of three stimulus types: speech, delexicalized speech,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d55669016fbdee09fdc903ca355afea9
https://pubmed.ncbi.nlm.nih.gov/33642942
https://pubmed.ncbi.nlm.nih.gov/33642942
Autor:
Izhak Shafran, Meysam Asgari
Publikováno v:
Computer Speech & Language. 47:298-313
Acoustic properties of speech samples can provide important cues in the assessment of voice pathology and cognitive function. The goal of this study is to develop novel algorithms for robust and accurate estimation of speech features and employ them
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d26fc052104988705875e21394ba534f
https://doi.org/10.1016/j.csl.2017.08.005
https://doi.org/10.1016/j.csl.2017.08.005
Autor:
Kevin W. Wilson, Kean Chin, Chanwoo Kim, Bo Li, Ananya Misra, Ron Weiss, Ehsan Variani, Andrew W. Senior, Tara N. Sainath, Izhak Shafran, Michiel Bacchiani, Arun Narayanan
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 25:965-979
Multichannel automatic speech recognition (ASR) systems commonly separate speech enhancement, including localization, beamforming, and postfiltering, from acoustic modeling. In this paper, we perform multichannel enhancement jointly with acoustic mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::76aebae59058bc33089ab1f3a1b12fcf
https://doi.org/10.1109/taslp.2017.2672401
https://doi.org/10.1109/taslp.2017.2672401
Publikováno v:
EMNLP/IJCNLP (1)
Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019) to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). It tackles the challenge of large label space and limited training data using a hierarchi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::150fa110eb5d61704fb21c4385fd1c1e
http://arxiv.org/abs/1908.11536
http://arxiv.org/abs/1908.11536
Autor:
Itay Laish, Avinatan Hassidim, Idan Szpektor, Izhak Shafran, Genady Beryozkin, Tzvika Hartman, Yossi Matias, Gang Li, Ido Cohn
Publikováno v:
NAACL-HLT (2)
Named Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9bdf1fd0081c5bfe00c1aface6cbbec6
http://arxiv.org/abs/1903.07037
http://arxiv.org/abs/1903.07037
Publikováno v:
ACL (1)
This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status. Lack of any publicly available corpus in this privacy-sensitive domain led us to develop ou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a57151cd51d5051cc4512efd2181795e
https://doi.org/10.18653/v1/p19-1087
https://doi.org/10.18653/v1/p19-1087