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
Privacy preserving encrypted phonetic search of speech data
Cornelius Glackin,Gérard Chollet,Nazim Dugan,Nigel Cannings,Julie Wall,Shahzaib Tahir,Indranil Ghosh Ray,Muttukrishnan Rajarajan +7 more
TL;DR: The approach advocates a demarcation of responsibilities between the client and server-side components for performing the speech recognition task, which symbolically encodes the audio and encrypts the data before uploading to the server.
Book ChapterDOI
E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text
TL;DR: In this article, an end-to-end trainable (fully differentiable) method for multi-language scene text localization and recognition is proposed based on a single fully convolutional network (FCN) with shared layers for both tasks.
Proceedings ArticleDOI
Multi-channel attention for end-to-end speech recognition
TL;DR: This work proposes a sensory attention mechanism that is invariant to the channel ordering and only increases the overall parameter count by 0.09%, and demonstrates that even without re-training, this attention-equipped end-to-end model is able to deal with arbitrary numbers of input channels during inference.
Proceedings Article
Non-Autoregressive Dialog State Tracking
TL;DR: This paper proposed a non-autoregressive dialog state tracking (NADST) model, which can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set.
Journal ArticleDOI
EdgeDRNN: Recurrent Neural Network Accelerator for Edge Inference
TL;DR: A lightweight Gated Recurrent Unit (GRU)-based RNN accelerator called EdgeDRNN that is optimized for low-latency edge RNN inference with batch size of 1 and a wall plug power efficiency that is over 4X higher than the commercial edge AI platforms.
References
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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.