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
Lip Reading Sentences in the Wild
TL;DR: In this article, a "Watch, Listen, Attend, and Spell" (WLAS) network was proposed to recognize phrases and sentences being spoken by a talking face, with or without the audio.
Proceedings ArticleDOI
Recurrent Neural Aligner: An Encoder-Decoder Neural Network Model for Sequence to Sequence Mapping.
TL;DR: An encoder-decoder recurrent neural network model called Recurrent Neural Aligner (RNA) that can be used for sequence to sequence mapping tasks and achieves competitive accuracy without using an external language model nor doing beam search decoding is introduced.
Posted Content
Recent Developments on ESPnet Toolkit Boosted by Conformer
Pengcheng Guo,Florian Boyer,Xuankai Chang,Tomoki Hayashi,Yosuke Higuchi,Hirofumi Inaguma,Naoyuki Kamo,Chenda Li,Daniel Garcia-Romero,Jiatong Shi,Jing Shi,Shinji Watanabe,Kun Wei,Wangyou Zhang,Yuekai Zhang +14 more
TL;DR: This paper shows the results for a wide range of end- to-end speech processing applications, such as automatic speech recognition (ASR), speech translations (ST), speech separation (SS) and text-to-speech (TTS).
Proceedings ArticleDOI
Deep Contextualized Acoustic Representations for Semi-Supervised Speech Recognition
TL;DR: In this article, a semi-supervised automatic speech recognition (ASR) system is proposed to exploit a large amount of unlabeled audio data via representation learning, where they reconstruct a temporal slice of filterbank features from past and future context frames.
Proceedings Article
Gated recurrent convolution neural network for OCR
Jianfeng Wang,Xiaolin Hu +1 more
TL;DR: A new architecture named Gated RCNN (GRCNN) is proposed, inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network, which is combined with BLSTM to recognize text in natural images.
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.