Zobrazeno 1 - 10
of 204
pro vyhledávání: '"P. Schraner"'
Autor:
Paonessa, Claudio, Schraner, Yanick, Deriu, Jan, Hürlimann, Manuela, Vogel, Manfred, Cieliebak, Mark
This paper investigates the challenges in building Swiss German speech translation systems, specifically focusing on the impact of dialect diversity and differences between Swiss German and Standard German. Swiss German is a spoken language with no f
Externí odkaz:
http://arxiv.org/abs/2310.09088
Autor:
Plüss, Michel, Deriu, Jan, Schraner, Yanick, Paonessa, Claudio, Hartmann, Julia, Schmidt, Larissa, Scheller, Christian, Hürlimann, Manuela, Samardžić, Tanja, Vogel, Manfred, Cieliebak, Mark
We present STT4SG-350 (Speech-to-Text for Swiss German), a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, whic
Externí odkaz:
http://arxiv.org/abs/2305.18855
We present the results and findings of the 2nd Swiss German speech to Standard German text shared task at SwissText 2022. Participants were asked to build a sentence-level Swiss German speech to Standard German text system specialized on the Grisons
Externí odkaz:
http://arxiv.org/abs/2301.06790
Autor:
Schraner, Yanick
Reinforcement learning (rl) is a popular paradigm for sequential decision making problems. The past decade's advances in rl have led to breakthroughs in many challenging domains such as video games, board games, robotics, and chip design. The sample
Externí odkaz:
http://arxiv.org/abs/2210.17368
We present an in-depth evaluation of four commercially available Speech-to-Text (STT) systems for Swiss German. The systems are anonymized and referred to as system a-d in this report. We compare the four systems to our STT model, referred to as FHNW
Externí odkaz:
http://arxiv.org/abs/2207.00412
Autor:
Plüss, Michel, Hürlimann, Manuela, Cuny, Marc, Stöckli, Alla, Kapotis, Nikolaos, Hartmann, Julia, Ulasik, Malgorzata Anna, Scheller, Christian, Schraner, Yanick, Jain, Amit, Deriu, Jan, Cieliebak, Mark, Vogel, Manfred
We present SDS-200, a corpus of Swiss German dialectal speech with Standard German text translations, annotated with dialect, age, and gender information of the speakers. The dataset allows for training speech translation, dialect recognition, and sp
Externí odkaz:
http://arxiv.org/abs/2205.09501
Autor:
Janine Vetter, Guido Papa, Kurt Tobler, Javier M. Rodriguez, Manuel Kley, Michael Myers, Mahesa Wiesendanger, Elisabeth M. Schraner, Daniel Luque, Oscar R. Burrone, Cornel Fraefel, Catherine Eichwald
Publikováno v:
mBio, Vol 15, Iss 4 (2024)
ABSTRACTRotavirus (RV) replication takes place in the viroplasms, cytosolic inclusions that allow the synthesis of virus genome segments and their encapsidation in the core shell, followed by the addition of the second layer of the virion. The viropl
Externí odkaz:
https://doaj.org/article/fae7dd2c4b504156a84d282324252bcd
Reinforcement learning (RL) research focuses on general solutions that can be applied across different domains. This results in methods that RL practitioners can use in almost any domain. However, recent studies often lack the engineering steps ("tri
Externí odkaz:
http://arxiv.org/abs/2107.00703
Autor:
Guss, William Hebgen, Milani, Stephanie, Topin, Nicholay, Houghton, Brandon, Mohanty, Sharada, Melnik, Andrew, Harter, Augustin, Buschmaas, Benoit, Jaster, Bjarne, Berganski, Christoph, Heitkamp, Dennis, Henning, Marko, Ritter, Helge, Wu, Chengjie, Hao, Xiaotian, Lu, Yiming, Mao, Hangyu, Mao, Yihuan, Wang, Chao, Opanowicz, Michal, Kanervisto, Anssi, Schraner, Yanick, Scheller, Christian, Zhou, Xiren, Liu, Lu, Nishio, Daichi, Tsuneda, Toi, Ramanauskas, Karolis, Juceviciute, Gabija
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Despite this, a majority of challenges suffer from the same fundamental pr
Externí odkaz:
http://arxiv.org/abs/2106.03748
Sample inefficiency of deep reinforcement learning methods is a major obstacle for their use in real-world applications. In this work, we show how human demonstrations can improve final performance of agents on the Minecraft minigame ObtainDiamond wi
Externí odkaz:
http://arxiv.org/abs/2003.06066