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
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TL;DR: In this paper, the authors proposed an end-to-end speech framework for sequence labeling, by combining hierarchical CNNs with Connectionist Temporal Classification (CTC) directly without recurrent connections.
Proceedings ArticleDOI
Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis
TL;DR: Zhang et al. as discussed by the authors proposed the Bi-Bimodal Fusion Network (BBFN), which performs fusion (relevance increment) and separation (difference increment) on pairwise modality representations.
Proceedings ArticleDOI
Self-Training and Pre-Training are Complementary for Speech Recognition
Qiantong Xu,Alexei Baevski,Tatiana Likhomanenko,Paden Tomasello,Alexis Conneau,Ronan Collobert,Gabriel Synnaeve,Michael Auli +7 more
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Patent
Credit Card Auto-Fill
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Proceedings ArticleDOI
SignSpeaker: A Real-time, High-Precision SmartWatch-based Sign Language Translator
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References
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Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
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