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09/15/2023
Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition
We study a streamable attention-based encoder-decoder model in which eit...
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10/26/2022
Monotonic segmental attention for automatic speech recognition
We introduce a novel segmental-attention model for automatic speech reco...
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05/31/2021
Why does CTC result in peaky behavior?
The peaky behavior of CTC models is well known experimentally. However, ...
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04/13/2021
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept
With the advent of direct models in automatic speech recognition (ASR), ...
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04/12/2021
Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models
Attention-based encoder-decoder (AED) models learn an implicit internal ...
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04/07/2021
Librispeech Transducer Model with Internal Language Model Prior Correction
We present our transducer model on Librispeech. We study variants to inc...
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03/30/2021
A study of latent monotonic attention variants
End-to-end models reach state-of-the-art performance for speech recognit...
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05/19/2020
Investigations on Phoneme-Based End-To-End Speech Recognition
Common end-to-end models like CTC or encoder-decoder-attention models us...
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05/19/2020
A New Training Pipeline for an Improved Neural Transducer
The RNN transducer is a promising end-to-end model candidate. We compare...
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12/19/2019
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems
Recent advances in text-to-speech (TTS) led to the development of flexib...
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11/20/2019
On using 2D sequence-to-sequence models for speech recognition
Attention-based sequence-to-sequence models have shown promising results...
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11/20/2019
On Using SpecAugment for End-to-End Speech Translation
This work investigates a simple data augmentation technique, SpecAugment...
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05/10/2019
Language Modeling with Deep Transformers
We explore multi-layer autoregressive Transformer models in language mod...
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05/08/2019
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention - w/o Data Augmentation
We present state-of-the-art automatic speech recognition (ASR) systems e...
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05/14/2018
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
We compare the fast training and decoding speed of RETURNN of attention ...
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05/08/2018
Improved training of end-to-end attention models for speech recognition
Sequence-to-sequence attention-based models on subword units allow simpl...
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08/02/2016
RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks
In this work we release our extensible and easily configurable neural ne...
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06/22/2016