Joint CTC/attention decoding for end-to-end speech recognition
Takaaki Hori,Shinji Watanabe,John R. Hershey +2 more
- pp 518-529
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TLDR
This paper proposes joint decoding algorithm for end-to-end ASR with a hybrid CTC/attention architecture, which effectively utilizes both advantages in decoding.Abstract:
End-to-end automatic speech recognition (ASR) has become a popular alternative to conventional DNN/HMM systems because it avoids the need for linguistic resources such as pronunciation dictionary, tokenization, and context-dependency trees, leading to a greatly simplified model-building process. There are two major types of end-to-end architectures for ASR: attention-based methods use an attention mechanism to perform alignment between acoustic frames and recognized symbols, and connectionist temporal classification (CTC), uses Markov assumptions to efficiently solve sequential problems by dynamic programming. This paper proposes joint decoding algorithm for end-to-end ASR with a hybrid CTC/attention architecture, which effectively utilizes both advantages in decoding. We have applied the proposed method to two ASR benchmarks (spontaneous Japanese and Mandarin Chinese), and showing the comparable performance to conventional state-of-the-art DNN/HMM ASR systems without linguistic resources.read more
Citations
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Journal ArticleDOI
Hybrid CTC/Attention Architecture for End-to-End Speech Recognition
TL;DR: The proposed hybrid CTC/attention end-to-end ASR is applied to two large-scale ASR benchmarks, and exhibits performance that is comparable to conventional DNN/HMM ASR systems based on the advantages of both multiobjective learning and joint decoding without linguistic resources.
Journal ArticleDOI
Recent progresses in deep learning based acoustic models
TL;DR: In this paper, the authors summarize recent progress made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques, and further illustrate robustness issues in speech recognition systems, and discuss acoustic model adaptation, speech enhancement and separation.
Proceedings ArticleDOI
Iterative Alignment Network for Continuous Sign Language Recognition
TL;DR: The framework consists of a 3D convolutional residual network for feature learning and an encoder-decoder network with connectionist temporal classification (CTC) for sequence modelling that is optimized in an alternate way for weakly supervised continuous sign language recognition.
Proceedings ArticleDOI
Language independent end-to-end architecture for joint language identification and speech recognition
TL;DR: This paper presents a model that can recognize speech in 10 different languages, by directly performing grapheme (character/chunked-character) based speech recognition, based on the hybrid attention/connectionist temporal classification (CTC) architecture.
Proceedings ArticleDOI
End-to-end Speech Recognition With Word-Based Rnn Language Models
TL;DR: A novel word-based RNN-LM is proposed, which allows us to decode with only the word- based LM, where it provides look-ahead word probabilities to predict next characters instead of the character-based LM, leading competitive accuracy with less computation compared to the multi-level LM.
References
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Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
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Proceedings Article
The Kaldi Speech Recognition Toolkit
Daniel Povey,Arnab Ghoshal,Gilles Boulianne,Lukas Burget,Ondrej Glembek,Nagendra Kumar Goel,Mirko Hannemann,Petr Motlicek,Yanmin Qian,Petr Schwarz,Jan Silovsky,Georg Stemmer,Karel Vesely +12 more
TL;DR: The design of Kaldi is described, a free, open-source toolkit for speech recognition research that provides a speech recognition system based on finite-state automata together with detailed documentation and a comprehensive set of scripts for building complete recognition systems.
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