Proceedings ArticleDOI
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Alex Graves,Santiago Fernández,Faustino Gomez,Jürgen Schmidhuber +3 more
- pp 369-376
TLDR
This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems of sequence learning and post-processing.Abstract:
Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN.read more
Citations
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Proceedings ArticleDOI
Investigation of Modeling Units for Mandarin Speech Recognition Using Dfsmn-ctc-smbr
TL;DR: It is found that the proposed hybrid Character-Syllable modeling units is the best choice for CTC based acoustic modeling for Mandarin speech recognition in this work since it can dramatically reduce substitution errors in recognition results.
Journal ArticleDOI
Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture
TL;DR: This work introduces Candidate Fusion, a novel way to integrate an external language model to a sequence-to-sequence architecture that provides suggestions from an externallanguage knowledge, as a new input to the sequence- to-sequence recognizer.
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Efficient Knowledge Distillation for RNN-Transducer Models
Sankaran Panchapagesan,Daniel S. Park,Chung-Cheng Chiu,Yuan Shangguan,Qiao Liang,Alexander H. Gruenstein +5 more
TL;DR: This paper develops a distillation method for RNN-Transducer (RNN-T) models, a popular end-to-end neural network architecture for streaming speech recognition, and studies the effectiveness of the proposed approach in improving the accuracy of sparse Rnn-T models obtained by gradually pruning a larger uncompressed model, which also serves as the teacher during distillation.
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A comparable study of modeling units for end-to-end Mandarin speech recognition
TL;DR: The authors explored two major end-to-end models: connectionist temporal classification (CTC) model and attention-based encoder-decoder model for mandarin speech recognition.
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Deep Learning for Computer Architects
TL;DR: This text serves as a primer for computer architects in a new and rapidly evolving field of machine learning and recounts a variety of optimizations proposed recently to further improve future designs.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.