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
A Survey on Differential Privacy for Unstructured Data Content
Ying Zhao,Jinjun Chen +1 more
TL;DR: This survey summarizes and analyzes differential privacy solutions to protect unstructured data content before they are shared with untrusted parties, and concludes their privacy guarantees against AI attacks and utility losses.
Proceedings ArticleDOI
Multi-accent speech recognition with hierarchical grapheme based models
Kanishka Rao,Hasim Sak +1 more
TL;DR: This work trains grapheme-based acoustic models for speech recognition using a hierarchical recurrent neural network architecture with connectionist temporal classification (CTC) loss and observes large recognition accuracy improvements for Indian-accented utterances in Google VoiceSearch US traffic with a 40% relative WER reduction.
Proceedings ArticleDOI
ICFHR2014 Competition on Handwritten Text Recognition on Transcriptorium Datasets (HTRtS)
TL;DR: A contest on Handwritten Text Recognition organised in the context of the ICFHR 2014 conference is described and two tracks with increased freedom on the use of training data were proposed and three research groups participated in these two tracks.
Journal ArticleDOI
Progressive Joint Modeling in Unsupervised Single-Channel Overlapped Speech Recognition
TL;DR: This work proposes a modular structure on the neural network, applying a progressive pretraining regimen, and improving the objective function with transfer learning and a discriminative training criterion, which achieves over 30% relative improvement of word error rate.
Journal ArticleDOI
Speaker-Independent Silent Speech Recognition From Flesh-Point Articulatory Movements Using an LSTM Neural Network
TL;DR: This paper adopts a bidirectional long short-term memory recurrent neural network (BLSTM) as an articulatory model to effectively model the articulatory movements with long-range articulatory history and proposes Procrustes matching-based articulatory normalization by removing locational, rotational, and scaling differences.
References
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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.
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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.