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of 10
pro vyhledávání: '"Travadi, Ruchir"'
We propose a novel approach to optimizing a byte-level representation for end-to-end automatic speech recognition (ASR). Byte-level representation is often used by large scale multilingual ASR systems when the character set of the supported languages
Externí odkaz:
http://arxiv.org/abs/2406.09676
Autor:
Lei, Zhihong, Pusateri, Ernest, Han, Shiyi, Liu, Leo, Xu, Mingbin, Ng, Tim, Travadi, Ruchir, Zhang, Youyuan, Hannemann, Mirko, Siu, Man-Hung, Huang, Zhen
Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as contact names remains a challenge. In this work, we describe our person
Externí odkaz:
http://arxiv.org/abs/2310.09988
Autor:
Swietojanski, Pawel, Braun, Stefan, Can, Dogan, da Silva, Thiago Fraga, Ghoshal, Arnab, Hori, Takaaki, Hsiao, Roger, Mason, Henry, McDermott, Erik, Silovsky, Honza, Travadi, Ruchir, Zhuang, Xiaodan
Publikováno v:
International Conference on Acoustics, Speech, and Signal Processing, 2023 International Conference on Acoustics, Speech, and Signal Processing International Conference on Acoustics, Speech, and Signal Processing
This work studies the use of attention masking in transformer transducer based speech recognition for building a single configurable model for different deployment scenarios. We present a comprehensive set of experiments comparing fixed masking, wher
Externí odkaz:
http://arxiv.org/abs/2211.01438
The Listen, Attend and Spell (LAS) model and other attention-based automatic speech recognition (ASR) models have known limitations when operated in a fully online mode. In this paper, we analyze the online operation of LAS models to demonstrate that
Externí odkaz:
http://arxiv.org/abs/2008.05514
We propose an algorithm to extract noise-robust acoustic features from noisy speech. We use Total Variability Modeling in combination with Non-negative Matrix Factorization (NMF) to learn a total variability subspace and adapt NMF dictionaries for ea
Externí odkaz:
http://arxiv.org/abs/1907.06859
We propose Deep Multiset Canonical Correlation Analysis (dMCCA) as an extension to representation learning using CCA when the underlying signal is observed across multiple (more than two) modalities. We use deep learning framework to learn non-linear
Externí odkaz:
http://arxiv.org/abs/1904.01775
Autor:
Travadi, Ruchir, Narayanan, Shrikanth
Publikováno v:
In Computer Speech & Language January 2019 53:43-64
Akademický článek
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Akademický článek
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Autor:
Travadi, Ruchir, Saha, Goutam
Publikováno v:
2012 National Conference on Communications (NCC); 1/ 1/2012, p1-5, 5p