Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Paria Jamshid Lou"'
Autor:
Mark Johnson, Paria Jamshid Lou
Publikováno v:
ACL
Macquarie University
Macquarie University
Self-attentive neural syntactic parsers using contextualized word embeddings (e.g. ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detection in speech transcripts. Since the contextualized word embeddings are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fecccae2a25203405efd8cebdc92a6b6
https://doi.org/10.18653/v1/2020.acl-main.346
https://doi.org/10.18653/v1/2020.acl-main.346
Autor:
Mark Johnson, Paria Jamshid Lou
Publikováno v:
EMNLP (Findings)
Macquarie University
Macquarie University
Disfluency detection is usually an intermediate step between an automatic speech recognition (ASR) system and a downstream task. By contrast, this paper aims to investigate the task of end-to-end speech recognition and disfluency removal. We specific
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a932d7e5504ff3d09d4f86546f002ef5
https://doi.org/10.18653/v1/2020.findings-emnlp.186
https://doi.org/10.18653/v1/2020.findings-emnlp.186
This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO). The database includes 3000 semi-natural utterances, equivalent to 3 h and 25 min of speech data extracted from online radio plays. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b292621b52d594c1171432dbe6ecbbac
http://arxiv.org/abs/1906.01155
http://arxiv.org/abs/1906.01155
Publikováno v:
NAACL-HLT (1)
Macquarie University
Macquarie University
This paper studies the performance of a neural self-attentive parser on transcribed speech. Speech presents parsing challenges that do not appear in written text, such as the lack of punctuation and the presence of speech disfluencies (including fill
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a20b5ed2d09dcfa8e1124ad2cd98a17
https://doi.org/10.18653/v1/n19-1282
https://doi.org/10.18653/v1/n19-1282
Autor:
Paria Jamshid Lou, Mark Johnson
Publikováno v:
ACL (2)
This paper presents a model for disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel Model (NCM) to generate n-best candidate disfluency analyses and a Long Short-Term Memory (LSTM) la
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fad523dbbb1f4c63e31d3aa047319889
http://arxiv.org/abs/1808.09091
http://arxiv.org/abs/1808.09091
Publikováno v:
EMNLP
Macquarie University
Macquarie University
In recent years, the natural language processing community has moved away from task-specific feature engineering, i.e., researchers discovering ad-hoc feature representations for various tasks, in favor of general-purpose methods that learn the input
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42220f3ddb115841400855af1b98d183
Autor:
Thierauf, Christopher, Thielstrom, Ravenna, Oosterveld, Bradley, Becker, Will, Scheutz, Matthias
Publikováno v:
ACM Transactions on Human-Robot Interaction; Sep2024, Vol. 13 Issue 3, p1-23, 23p
Publikováno v:
Language Resources & Evaluation; Mar2019, Vol. 53 Issue 1, p1-16, 16p
Autor:
Romil Rawat, Rajesh Kumar Chakrawarti, Sanjaya Kumar Sarangi, Anand Rajavat, Mary Sowjanya Alamanda, Kotagiri Srividya, K. Sakthidasan Sankaran
This book reviews present state-of-the-art research related to the security of cloud computing including developments in conversational AI applications. It is particularly suited for those that bridge the academic world and industry, allowing readers