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
Advances in all-neural speech recognition
TL;DR: In this paper, a CTC-based all-neural (or end-to-end) speech recognizer was proposed, and a novel iterated-CTC method was used to transform a noisy initial output into a cleaner version.
Proceedings ArticleDOI
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality
TL;DR: This paper introduces a Chinese single- and multi-modal sentiment analysis dataset, CH-SIMS, which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations, and proposes a multi-task learning framework based on late fusion as the baseline.
Proceedings Article
Sequence labelling in structured domains with hierarchical recurrent neural networks
TL;DR: This paper presents a hierarchical system, based on the connectionist temporal classification algorithm, for labelling unsegmented sequential data at multiple scales with recurrent neural networks only and shows that the system outperforms hidden Markov models, while making fewer assumptions about the domain.
Journal ArticleDOI
Urdu Nastaliq recognition using convolutionalrecursive deep learning
Saeeda Naz,Arif Iqbal Umar,Riaz Ahmad,Imran Siddiqi,Saad Bin Ahmed,Muhammad Imran Razzak,Faisal Shafait +6 more
TL;DR: This work presents a hybrid approach based on explicit feature extraction by combining convolutional and recursive neural networks for feature learning and classification of cursive Urdu Nastaliq script using the proposed hierarchical combination of CNN and MDLSTM.
Proceedings ArticleDOI
Automatic Transcription of Handwritten Medieval Documents
Andreas Fischer,Markus Wuthrich,Marcus Liwicki,Volkmar Frinken,Horst Bunke,Gabriel Viehhauser,Michael Stolz +6 more
TL;DR: Two state-of-the art recognizers originally developed for modern scripts are applied to medieval documents, one based on Hidden Markov Models and the second based on a Neural Network with a bidirectional Long Short-Term Memory.
References
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