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
End-to-end Speech Recognition Using Lattice-free MMI.
TL;DR: The work on end-to-end training of acoustic models using the lattice-free maximum mutual information (LF-MMI) objective function in the context of hidden Markov models shows that this approach can achieve comparable results to regular LF-M MI on well-known large vocabulary tasks.
Proceedings ArticleDOI
Improving CNN-RNN Hybrid Networks for Handwriting Recognition
TL;DR: A modified CNN-RNN hybrid architecture is proposed with a major focus on effective training using: (i) efficient initialization of network using synthetic data for pretraining, (ii) image normalization for slant correction and (iii) domain specific data transformation and distortion for learning important invariances.
Proceedings ArticleDOI
Acoustic modelling with CD-CTC-SMBR LSTM RNNS
TL;DR: This paper describes a series of experiments to extend the application of Context-Dependent long short-term memory recurrent neural networks (RNNs) trained with Connectionist Temporal Classification (CTC) and sMBR loss and investigates transferring knowledge from one network to another through alignments.
Proceedings ArticleDOI
D3TW: Discriminative Differentiable Dynamic Time Warping for Weakly Supervised Action Alignment and Segmentation
TL;DR: The proposed Discriminative Differentiable Dynamic Time Warping (D3TW) innovatively solves sequence alignment with discriminative modeling and end-to-end training, which substantially improves the performance in weakly supervised action alignment and segmentation tasks.
Posted Content
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context
Wei Han,Zhengdong Zhang,Yu Zhang,Jiahui Yu,Chung-Cheng Chiu,James Qin,Anmol Gulati,Ruoming Pang,Yonghui Wu +8 more
TL;DR: This paper proposes a simple scaling method that scales the widths of ContextNet that achieves good trade-off between computation and accuracy and demonstrates that on the widely used LibriSpeech benchmark, ContextNet achieves a word error rate of 2.1%/4.6%.
References
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Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
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