Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Rongzhong Lian"'
Publikováno v:
CAAI Artificial Intelligence Research, Vol 1, Iss 1, Pp 1-7 (2022)
Robustness is a long-standing challenge for automatic speech recognition (ASR) as the applied environment of any ASR system faces much noisier speech samples than clean training corpora. However, it is impractical to annotate every types of noisy env
Externí odkaz:
https://doaj.org/article/2d317b44290a488f8148913eb4c7f2cd
Autor:
Rongzhong Lian, Lixin Fan, Chen Zhang, Jinhua Peng, Chaotao Chen, Yawen Li, Lei Chen, Di Jiang
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 35:3261-3265
With the prevalence of voice devices in our daily life, speech data is accumulated at an unprecedented speed. The vast amount of speech data form an invaluable database for security surveillance and financial risk management. However, the speeches co
Autor:
Yuanfeng Song, Rongzhong Lian, Yixin Chen, Di Jiang, Xuefang Zhao, Conghui Tan, Qian Xu, Raymond Chi-Wing Wong
Publikováno v:
Proceedings of the 30th ACM International Conference on Multimedia.
Autor:
Rongzhong Lian, Weiwei Zhao, Di Jiang, Qiang Yang, Qian Xu, Yongxin Tong, Jinhua Peng, Xueyang Wu, Yuanfeng Song
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 12:1-22
Probabilistic topic modeling has been applied in a variety of industrial applications. Training a high-quality model usually requires a massive amount of data to provide comprehensive co-occurrence information for the model to learn. However, industr
Autor:
Chaotao Chen, Di Jiang, Jinhua Peng, Rongzhong Lian, Chen Jason Zhang, Qian Xu, Lixin Fan, Qiang Yang
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:16004-16006
We demonstrate a health-friendly speaker verification system for voice-based identity verification on mobile devices. The system is built upon a speech processing module, a ResNet-based local acoustic feature extractor and a multi-head attention-base
Autor:
Di Jiang, Rongzhong Lian, Lei Chen, Hua Wu, Jinhua Peng, Chen Zhang, Yuanfeng Song, Huang He, Siqi Bao
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783030731991
DASFAA (3)
DASFAA (3)
In this paper, we propose a configurable topic modeling framework named Familia. Familia supports an important line of topic models that are widely applicable in text engineering scenarios. In order to relieve burdens of software engineers without kn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac56ebcf0f15aee4af04055cc7d88f5f
https://doi.org/10.1007/978-3-030-73200-4_36
https://doi.org/10.1007/978-3-030-73200-4_36
Autor:
Di Jiang, Yongxin Tong, Jinhua Peng, Rongzhong Lian, Weiwei Zhao, Huaxiao Mo, Yuanfeng Song, Qian Xu, Chaotao Chen, Conghui Tan
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783030594183
DASFAA (3)
DASFAA (3)
Traditional Automatic Speech Recognition (ASR) systems are usually trained with speech records centralized on the ASR vendor’s machines. However, with data regulations such as General Data Protection Regulation (GDPR) coming into force, sensitive d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68698cc9b1eda3e8ccb7b07aac69db0c
https://doi.org/10.1007/978-3-030-59419-0_54
https://doi.org/10.1007/978-3-030-59419-0_54
Autor:
Somdeb Majumdar, Zhengfei Wang, Lance Rane, Zeyang Yu, Marcel Salathé, Aditya Bhatt, Ruihan Yang, Aleksandra Malysheva, Fan Wang, Łukasz Kidziński, Jeremy D. Watson, Bo Zhou, Rongzhong Lian, Aleksei Shpilman, Zhen Wang, Sharada P. Mohanty, Sergey Kolesnikov, Jennifer L. Hicks, Quan Yuan, Hao Tian, Carmichael F. Ong, Wojciech Jaśkowski, Yunsheng Tian, Nihat Engin Toklu, Shauharda Khadka, Minghui Qiu, Yinyin Liu, Sean F. Carroll, Ivan Sosin, Rupesh Kumar Srivastava, Xu Hu, Evren Tumer, Pranav Shyam, Scott L. Delp, Oleg Svidchenko, Daniel Kudenko, Oleksii Hrinchuk, Odd Rune Lykkebø, Wenxin Li, Sergey Levine, Mattias Ljungström, Pingchuan Ma, Zehong Hu, Hongsheng Zeng, Anton Pechenko, Zach Dwiel, Jun Huang, Peng Peng, Penghui Qi, Garrett Andersen
Publikováno v:
The NeurIPS '18 Competition ISBN: 9783030291341
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants described their algo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d4e062dd76b67ed52e1d9e01a8e0900
https://doi.org/10.1007/978-3-030-29135-8_4
https://doi.org/10.1007/978-3-030-29135-8_4
Publikováno v:
The NeurIPS '18 Competition ISBN: 9783030291341
Developing efficient walking gaits for biomechanical robots is a difficult task that requires optimizing parameters in a continuous, multidimensional space. In this paper we present a new framework for learning complex gaits with musculoskeletal mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::855bf48f9c88fff18c59695f224956fe
https://doi.org/10.1007/978-3-030-29135-8_10
https://doi.org/10.1007/978-3-030-29135-8_10
Publikováno v:
Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation.
In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we prop