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
More filters
Proceedings ArticleDOI
Multilingual OCR for Indic Scripts
TL;DR: An end-to-end RNN based architecture which can detect the script and recognize the text in a segmentation-free manner is proposed for this purpose and demonstrated for 12 Indian languages and English.
Proceedings ArticleDOI
Learning from the Master: Distilling Cross-modal Advanced Knowledge for Lip Reading
TL;DR: In this article, a trainable "master" network was proposed to extract bi-modal knowledge from audio signals and silent lip videos instead of a pretrained teacher, and the master produces logits from three modalities of features: audio modality, video modality and their combination.
Proceedings ArticleDOI
Sequence Noise Injected Training for End-to-end Speech Recognition
TL;DR: A simple noise injection algorithm for training end-to-end ASR models which consists in adding to the spectra of training utterances the scaled spectraof random utterances of comparable length is presented.
Posted Content
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation
TL;DR: A Fused Acoustic and Text Masked Language Model (FATMLM) is proposed which jointly learns a unified representation for both acoustic and text input from various types of corpora including parallel data for speech recognition and machine translation, and even pure speech and text data.
Proceedings ArticleDOI
ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard
Rui Zhang,Mingkun Yang,Xiang Bai,Baoguang Shi,Dimosthenis Karatzas,Shijian Lu,C. V. Jawahar,Yongsheng Zhou,Qianyi Jiang,Qi Song,Nan Li,Kai Zhou,Lei Wang,Dong Wang,Minghui Liao +14 more
TL;DR: The ICDAR2019-ReCTS this article, which mainly focuses on reading Chinese text on signboard, has attracted great interest and the final results of the competition are presented in this article.
References
More filters
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.