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
Duration Robust Weakly Supervised Sound Event Detection
Heinrich Dinkel,Kai Yu +1 more
TL;DR: It is shown that for this task subsampling the temporal resolution by a neural network enhances the F1 score as well as its robustness towards short, sporadic sound events and the use of double thresholding as a more robust and predictable post-processing method.
Proceedings ArticleDOI
OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold
Mohamed Yousef,Tom E. Bishop +1 more
TL;DR: The authors proposed a novel and simple neural network module, termed OrigamiNet, that can augment any CTC-trained, fully convolutional single line text recognizer, to convert it into a multi-line version by providing the model with enough spatial capacity to be able to properly collapse a 2D input signal into 1D without losing information.
Proceedings ArticleDOI
WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit
Binbin Zhang,Di Wu,Zhendong Peng,Xingchen Song,Zhuoyuan Yao,Hang Lv,Lei Xie,Chao Yang,Fuping Pan,Jianwei Niu +9 more
TL;DR: The brand-new WeNet 2.0 achieves up to 10% relative recognition performance improvement over the original WeNet on various corpora and makes available several important production-oriented features.
Patent
Latency constraints for acoustic modeling
TL;DR: In this article, the authors proposed a method to determine the occurrence of a phone at any of multiple time frames within a maximum delay of receiving audio data corresponding to the phone, using output of the trained neural network to determine a transcription for the utterance.
Proceedings ArticleDOI
Scanning Neural Network for Text Line Recognition
TL;DR: A segmentation free text line recognition approach using multi layer perceptron (MLP) and hidden markov models (HMMs) that achieves 98.4% character recognition accuracy that is statistically significantly better in comparison with character recognition accuracies obtained from state-of-the-art open source OCR systems.
References
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Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
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