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
American Sign Language Fingerspelling Recognition in the Wild
Bowen Shi,Aurora Martinez Del Rio,Jonathan Keane,Jonathan Michaux,Diane Brentari,Greg Shakhnarovich,Karen Livescu +6 more
TL;DR: In this article, the authors presented the first attempt to recognize fingerspelling sequences in this challenging setting, using videos collected from websites and trained a special-purpose signing hand detector using a small subset of their data.
Proceedings Article
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information.
TL;DR: In this article, a new algorithm based on the generative adversarial imitation learning framework is proposed to automatically learn sub-task policies from unsegmented demonstrations by maximizing the directed information flow in the graphical model between latent variables and their generated trajectories.
Proceedings ArticleDOI
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model
TL;DR: A novel Adversarial Training approach for end-to-end speech recognition using a Criticizing Language Model (CLM) so the CLM and the automatic speech recognition model can challenge and learn from each other iteratively to improve the performance.
Book ChapterDOI
TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network
TL;DR: An end-to-end trainable network architecture, named TextNet, is proposed, which is able to simultaneously localize and recognize irregular text from images, and can achieve state-of-the-art performance on irregular datasets by a large margin.
Book ChapterDOI
LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis.
Zejiang Shen,Ruochen Zhang,Melissa Dell,Benjamin Charles Germain Lee,Jacob C. Carlson,Weining Li +5 more
TL;DR: The LayoutParser library as mentioned in this paper is an open-source library for streamlining the usage of deep learning in document image analysis research and applications, which includes a set of simple and intuitive interfaces for applying and customizing DL models for layout detection, character recognition, and many other document processing tasks.
References
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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
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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.