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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Journal ArticleDOI
Noise robust ASR in reverberated multisource environments applying convolutive NMF and Long Short-Term Memory
TL;DR: A novel front-end for context-sensitive Tandem feature extraction is designed and it is shown how the Connectionist Temporal Classification approach can be used as a BLSTM-based back-end, alternatively to Hidden Markov Models (HMM).
Journal ArticleDOI
OCR4all -- An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings
Christian Reul,Dennis Christ,Alexander Hartelt,Nico Balbach,Maximilian Wehner,Uwe Springmann,Christoph Wick,Christine Grundig,Andreas Büttner,Frank Puppe +9 more
TL;DR: OCR4all as mentioned in this paper is an open-source OCR software that combines state-of-the-art OCR components and continuous model training into a comprehensive workflow for historical printings.
Proceedings ArticleDOI
End-to-End Optical Music Recognition Using Neural Networks.
TL;DR: Results obtained depict classification error rates around 2 % at symbol level, thus proving the potential of the proposed end-to-end architecture for OMR.
Journal Article
Differentiable Weighted Finite-State Transducers
TL;DR: A framework for automatic differentiation with weighted finite-state transducers (WFSTs) allowing them to be used dynamically at training time and a convolutional WFST layer which maps lower-level representations to higher- level representations and can be used as a drop-in replacement for a traditional convolution.
Book ChapterDOI
Audio and visual modality combination in speech processing applications
Gerasimos Potamianos,Etienne Marcheret,Youssef Mroueh,Vaibhava Goel,Alexandros Koumbaroulis,Argyrios Vartholomaios,Spyridon Thermos +6 more
TL;DR: This chapter focuses on AVASR while also addressing other related problems, namely audio-visual speech activity detection, diarization, and synchrony detection and rapid recent advances, leading to so-called "end-to-end" AVAsR systems.
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.