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
Cascaded Encoders for Unifying Streaming and Non-Streaming ASR
Arun Narayanan,Tara N. Sainath,Ruoming Pang,Jiahui Yu,Chung-Cheng Chiu,Rohit Prabhavalkar,Ehsan Variani,Trevor Strohman +7 more
TL;DR: In this paper, a cascaded encoder-decoder model is proposed to operate in both streaming and non-streaming modes simultaneously, which achieves similar word error rates (WER) as a standalone streaming model when operating in streaming mode, and obtains 10% -27% relative improvement when operating on nonstreaming mode.
Proceedings ArticleDOI
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020
TL;DR: In the IWSLT 2020 offline speech translation task, FBK as mentioned in this paper used an end-to-end model based on an adaptation of the Transformer for speech data to translate English TED talks audio into German texts.
Proceedings ArticleDOI
Sequence-discriminative training of recurrent neural networks
TL;DR: It is shown that although recurrent neural networks already make use of the whole observation sequence and are able to incorporate more contextual information than feed forward networks, their performance can be improved with sequence-discriminative training.
Proceedings ArticleDOI
Leveraging Unpaired Text Data for Training End-To-End Speech-to-Intent Systems
Yinghui Huang,Hong-Kwang Kuo,Samuel Thomas,Zvi Kons,Kartik Audhkhasi,Brian Kingsbury,Ron Hoory,Michael Picheny +7 more
TL;DR: In this paper, a CTC-based speech-to-intent (S2I) system was proposed to leverage NLU text resources, where acoustic embeddings for intent classification were tied to fine-tuned BERT text embedding and data augmentation was performed using a multi-speaker text-tospeech system.
Proceedings ArticleDOI
Can we build language-independent OCR using LSTM networks?
Adnan Ul-Hasan,Thomas M. Breuel +1 more
TL;DR: The question to what extent LSTM models can be used for multilingual OCR without the use of language models is explored, and cross-language performance of L STM models trained on different languages is measured.
References
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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.
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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.