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 Article
Towards End-To-End Speech Recognition with Recurrent Neural Networks
Alex Graves,Navdeep Jaitly +1 more
TL;DR: A speech recognition system that directly transcribes audio data with text, without requiring an intermediate phonetic representation is presented, based on a combination of the deep bidirectional LSTM recurrent neural network architecture and the Connectionist Temporal Classification objective function.
Book
Supervised Sequence Labelling with Recurrent Neural Networks
TL;DR: A new type of output layer that allows recurrent networks to be trained directly for sequence labelling tasks where the alignment between the inputs and the labels is unknown, and an extension of the long short-term memory network architecture to multidimensional data, such as images and video sequences.
End to end speech recognition in English and Mandarin
Dario Amodei,Rishita Anubhai,Eric Battenberg,Carl Case,Jared Casper,Bryan Catanzaro,Jingdong Chen,Mike Chrzanowski,Adam Coates,Greg Diamos,Erich Elsen,Jesse Engel,Linxi Fan,Christopher Fougner,Tony X. Han,Awni Hannun,Billy Jun,Patrick LeGresley,Libby Lin,Sharan Narang,Andrew Y. Ng,Sherjil Ozair,Ryan Prenger,Jonathan Raiman,Sanjeev Satheesh,David Seetapun,Shubho Sengupta,Yi Wang,Zhiqian Wang,Chong Wang,Bo Xiao,Dani Yogatama,Jun Zhan,Zhenyao Zhu +33 more
TL;DR: It is shown that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech-two vastly different languages, and is competitive with the transcription of human workers when benchmarked on standard datasets.
Posted Content
Deep Speech: Scaling up end-to-end speech recognition
Awni Hannun,Carl Case,Jared Casper,Bryan Catanzaro,Greg Diamos,Erich Elsen,Ryan Prenger,Sanjeev Satheesh,Shubho Sengupta,Adam Coates,Andrew Y. Ng +10 more
TL;DR: Deep Speech, a state-of-the-art speech recognition system developed using end-to-end deep learning, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set.
Journal ArticleDOI
A Novel Connectionist System for Unconstrained Handwriting Recognition
TL;DR: This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies, significantly outperforming a state-of-the-art HMM-based system.
References
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