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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Dissertation
On internal language representations in deep learning : an analysis of machine translation and speech recognition
TL;DR: A unified methodology for evaluating internal representations in neural networks, consisting of three steps: training a model on a complex end-to-end task; generating feature representations from different parts of the trained model; and training classifiers on simple supervised learning tasks using the representations.
Proceedings ArticleDOI
Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition.
TL;DR: Li et al. as discussed by the authors proposed a non-autoregressive end-to-end speech recognition system called LASO (listen attentively, and spell once) to predict a textual token in the sequence without the dependence on other tokens.
Proceedings ArticleDOI
ICDAR 2015 competition HTRtS: Handwritten Text Recognition on the tranScriptorium dataset
TL;DR: This paper describes the second edition of the Handwritten Text Recognition (HTR) contest on the tranScriptorium datasets that has been held in the context of the International Conference on Document Analysis and Recognition 2015.
Proceedings ArticleDOI
A Compact CNN-DBLSTM Based Character Model for Offline Handwriting Recognition with Tucker Decomposition
TL;DR: The results show that using Tucker decomposition alone offers a good solution to building a compact CNN-DBLSTM model which can reduce significantly both the footprint and latency yet without degrading recognition accuracy.
Proceedings ArticleDOI
End-to-End Speech Command Recognition with Capsule Network
Jaesung Bae,Dae-Shik Kim +1 more
TL;DR: This work proposes an end-to-end SR system with capsule networks to capture the spatial relationship and pose information of speech spectrogram features in both frequency and time axes and shows that the proposed system achieves better results on both clean and noise-added test than baseline CNN models.
References
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Journal ArticleDOI
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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.