Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Tzeviya Sylvia Fuchs"'
Autor:
Tzeviya Sylvia Fuchs, Yedid Hoshen
Unsupervised word segmentation in audio utterances is challenging as, in speech, there is typically no gap between words. In a preliminary experiment, we show that recent deep self-supervised features are very effective for word segmentation but requ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e21118851485b48b7488f4884b416e46
http://arxiv.org/abs/2304.00993
http://arxiv.org/abs/2304.00993
Publikováno v:
ICASSP
In this paper, we propose a spoken term detection algorithm for simultaneous prediction and localization of in-vocabulary and out-of-vocabulary terms within an audio segment. The proposed algorithm infers whether a term was uttered within a given spe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dada47904713423a312a75fbae72690a
http://arxiv.org/abs/2103.05468
http://arxiv.org/abs/2103.05468
Autor:
Tzeviya Sylvia Fuchs, Joseph Keshet
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. 11:1310-1317
Spoken term detection (STD) is the task of determining whether and where a given word or phrase appears in a given segment of speech. Algorithms for STD are often aimed at maximizing the gap between the scores of positive and negative examples. As su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f743f52129be21e9243e3600b8925736
https://doi.org/10.1109/jstsp.2017.2764268
https://doi.org/10.1109/jstsp.2017.2764268
Publikováno v:
International journal of speech-language pathology. 20(6)
Investigating speech processes often involves analysing data gathered by phonetically annotating speech recordings. Yet, the manual annotation of speech can often be resource intensive-requiring substantial time and labour to complete. Recent advance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c640762924834f168cd5428b7fbf5fcc
https://pubmed.ncbi.nlm.nih.gov/31274358
https://pubmed.ncbi.nlm.nih.gov/31274358
Publikováno v:
INTERSPEECH
In this paper, we propose to apply object detection methods from the vision domain on the speech recognition domain, by treating audio fragments as objects. More specifically, we present SpeechYOLO, which is inspired by the YOLO algorithm for object
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d729154865cc0611ddb15b2fd45830e9
http://arxiv.org/abs/1904.07704
http://arxiv.org/abs/1904.07704
Autor:
Matthew Goldrick, Vijay A. Mittal, Kasia Hitczenko, Yael Segal, Joseph Keshet, Tzeviya Sylvia Fuchs
Publikováno v:
The Journal of the Acoustical Society of America. 148:2584-2584
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::86af0d660e4625b8fd53b4dbffdc73f0
https://doi.org/10.1121/1.5147177
https://doi.org/10.1121/1.5147177
Publikováno v:
Computación y Sistemas. 22
Sentiment analysis deals with classifying written texts according to their polarity. Previous research in this topic has been conducted mostly for Latin languages, and no research has been done for Hebrew. This is important because it turns out that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::629ea93ab2b513eac31b7e9828b60aae
https://doi.org/10.13053/cys-22-4-3071
https://doi.org/10.13053/cys-22-4-3071