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Proceedings ArticleDOI

Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

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.

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Journal ArticleDOI

Multi-way, multilingual neural machine translation

TL;DR: The first attention-based neural-MT for multi-way, multilingual translation is proposed and it outperforms strong conventional statistical machine translation systems on Turkish-English and Uzbek-English by incorporating the resources of other language pairs.
Proceedings ArticleDOI

Learning the PE Header, Malware Detection with Minimal Domain Knowledge

TL;DR: This work explores the feasibility of applying neural networks to malware detection and feature learning by restricting ourselves to a minimal amount of domain knowledge in order to extract a portion of the Portable Executable (PE) header.
Proceedings Article

Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction

TL;DR: This work introduces Audio-Visual Hidden Unit BERT (AV-HuBERT), a self-supervised representation learning framework for audio-visual speech, which masks multi-stream video input and predicts automatically discovered and iteratively refined multimodal hidden units.
Proceedings ArticleDOI

Intermediate Loss Regularization for CTC-Based Speech Recognition

TL;DR: In this article, an intermediate CTC loss is proposed to regularize CTC training and improve the performance with only small modification of the code and small and no overhead during training and inference.
Proceedings ArticleDOI

A Comparison of End-to-End Models for Long-Form Speech Recognition

TL;DR: This paper investigates and improves the performance of end-to-end models on long-form transcription and explores two improvements to attention-based systems that significantly improve its performance: restricting the attention to be monotonic, and applying a novel decoding algorithm that breaks long utterances into shorter overlapping segments.
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.
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