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
Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
Jordi Silvestre-Ryan,Ian Holmes +1 more
TL;DR: PoreOver as mentioned in this paper improves the accuracy of base calling with Oxford Nanopore's 1D2 and related sequencing protocols by aligning their probability profiles, and is compatible with multiple nanopore basecallers.
Journal ArticleDOI
Learning Deep Representations for Video-Based Intake Gesture Detection
TL;DR: In this article, a deep learning architecture is applied to the problem of video-based detection of intake gestures during eating occasions, and the best model achieves an $F_1$ score of 0.858, appearance features contribute more than motion features, and temporal context in form of multiple video frames is essential for top model performance.
Proceedings ArticleDOI
Speaker Adaptation for Attention-Based End-to-End Speech Recognition
TL;DR: Three regularization-based speaker adaptation approaches to adapt the attention-based encoder-decoder (AED) model with very limited adaptation data from target speakers for end-to-end automatic speech recognition are proposed.
Journal ArticleDOI
Deep Belief Neural Networks and Bidirectional Long-Short Term Memory Hybrid for Speech Recognition
Łukasz Brocki,Krzysztof Marasek +1 more
TL;DR: Results show that the use of the new DBNN-BLSTM hybrid as the acoustic model for the Large Vocabulary Continuous Speech Recognition (LVCSR) increases word recognition accuracy and has many parameters and in some cases it may suffer performance issues in real-time applications.
Posted Content
Self-Delimiting Neural Networks
TL;DR: To apply AOPS to (possibly recurrent) neural networks (NNs) and to efficiently teach a SLIM NN to solve many tasks, each connection keeps a list of tasks it is used for, which may be efficiently updated during training.
References
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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.
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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.