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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Improving Generalization of Transformer for Speech Recognition with Parallel Schedule Sampling and Relative Positional Embedding.
TL;DR: This work proposes to use parallel schedule sampling (PSS) and relative positional embedding (RPE) to help transformer generalize to unseen data and achieves 7% relative improvement for short utterances and 30% absolute gains for long utterances on a 10,000-hour ASR task.
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Describing Multimedia Content using Attention-based Encoder--Decoder Networks
TL;DR: In this article, the authors describe systems that learn to attend to different places in the input, for each element of the output, for a variety of tasks: machine translation, image caption generation, video clip description, and speech recognition, based on a shared set of building blocks: gated recurrent neural networks and convolutional neural networks, along with trained attention mechanisms.
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DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation
TL;DR: This paper proposes an ASR approach with efficient gradient-based architecture search, DARTS-ASR, and applies this approach not only on many languages to perform monolingual ASR, but also on a multilingual ASR setting.
Proceedings ArticleDOI
LipType: A Silent Speech Recognizer Augmented with an Independent Repair Model
Laxmi Pandey,Ahmed Sabbir Arif +1 more
TL;DR: In this article, an optimized version of LipNet for improved speed and accuracy is developed, and an independent repair model that processes video input for poor lighting conditions, when applicable, and corrects potential errors in output for increased accuracy.
Proceedings ArticleDOI
An Analysis of Local Monotonic Attention Variants.
TL;DR: A simple technique to implement windowed attention, which can be applied on top of an existing global attention model, and it is shown that the proposed model can be trained from random initialization and achieve results comparable to the global attention baseline.
References
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