b'Alex Waibel'

research

∙ 05/24/2022

Adaptive multilingual speech recognition with pretrained models

Multilingual speech recognition with supervised learning has achieved gr...

Ngoc-Quan Pham, et al. ∙

research

∙ 08/16/2021

Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages

We translate a closed text that is known in advance and available in man...

Zhong Zhou, et al. ∙

research

∙ 04/12/2021

Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Machine Translation

We translate a closed text that is known in advance into a severely low ...

Zhong Zhou, et al. ∙

research

∙ 10/07/2020

Super-Human Performance in Online Low-latency Recognition of Conversational Speech

Achieving super-human performance in recognizing human speech has been a...

Thai-Son Nguyen, et al. ∙

research

∙ 04/08/2020

Error-correction and extraction in request dialogs

We propose a component that gets a request and a correction and outputs ...

Stefan Constantin, et al. ∙

research

∙ 03/22/2020

High Performance Sequence-to-Sequence Model for Streaming Speech Recognition

Recently sequence-to-sequence models have started to achieve state-of-th...

Thai-Son Nguyen, et al. ∙

research

∙ 03/22/2020

Low Latency ASR for Simultaneous Speech Translation

User studies have shown that reducing the latency of our simultaneous le...

Thai-Son Nguyen, et al. ∙

research

∙ 03/09/2020

Toward Cross-Domain Speech Recognition with End-to-End Models

In the area of multi-domain speech recognition, research in the past foc...

Thai-Son Nguyen, et al. ∙

research

∙ 12/09/2019

An Interactive Indoor Drone Assistant

With the rapid advance of sophisticated control algorithms, the capabili...

Tino Fuhrman, et al. ∙

research

∙ 11/29/2019

Bimodal Speech Emotion Recognition Using Pre-Trained Language Models

Speech emotion recognition is a challenging task and an important step t...

Verena Heusser, et al. ∙

research

∙ 11/07/2019

Low-Resource Machine Translation using Interlinear Glosses

Neural Machine Translation (NMT) does not handle low-resource translatio...

Zhong Zhou, et al. ∙

research

∙ 10/29/2019

Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation

Sequence-to-Sequence (S2S) models recently started to show state-of-the-...

Thai-Son Nguyen, et al. ∙

research

∙ 09/30/2019

Incremental processing of noisy user utterances in the spoken language understanding task

The state-of-the-art neural network architectures make it possible to cr...

Stefan Constantin, et al. ∙

research

∙ 06/20/2019

Improving Zero-shot Translation with Language-Independent Constraints

An important concern in training multilingual neural machine translation...

Ngoc-Quan Pham, et al. ∙

research

∙ 06/04/2019

Self-Attentional Models for Lattice Inputs

Lattices are an efficient and effective method to encode ambiguity of up...

Matthias Sperber, et al. ∙

research

∙ 06/03/2019

Fluent Translations from Disfluent Speech in End-to-End Speech Translation

Spoken language translation applications for speech suffer due to conver...

Elizabeth Salesky, et al. ∙

research

∙ 04/30/2019

Very Deep Self-Attention Networks for End-to-End Speech Recognition

Recently, end-to-end sequence-to-sequence models for speech recognition ...

Ngoc-Quan Pham, et al. ∙

research

∙ 04/15/2019

Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation

Speech translation has traditionally been approached through cascaded mo...

Matthias Sperber, et al. ∙

research

∙ 03/31/2019

Learning Shared Encoding Representation for End-to-End Speech Recognition Models

In this work, we learn a shared encoding representation for a multi-task...

Thai-Son Nguyen, et al. ∙

research

∙ 02/02/2019

Using multi-task learning to improve the performance of acoustic-to-word and conventional hybrid models

Acoustic-to-word (A2W) models that allow direct mapping from acoustic si...

Thai-Son Nguyen, et al. ∙

research

∙ 12/17/2018

Multi-task learning to improve natural language understanding

Recently advancements in sequence-to-sequence neural network architectur...

Stefan Constantin, et al. ∙

research

∙ 11/07/2018

Towards Fluent Translations from Disfluent Speech

When translating from speech, special consideration for conversational s...

Elizabeth Salesky, et al. ∙

research

∙ 09/10/2018

Towards one-shot learning for rare-word translation with external experts

Neural machine translation (NMT) has significantly improved the quality ...

Ngoc-Quan Pham, et al. ∙

research

∙ 08/25/2018

Paraphrases as Foreign Languages in Multilingual Neural Machine Translation

Using paraphrases, the expression of the same semantic meaning in differ...

Zhong Zhou, et al. ∙

research

∙ 08/01/2018

Low-Latency Neural Speech Translation

Through the development of neural machine translation, the quality of ma...

Jan Niehues, et al. ∙

research

∙ 07/27/2018

A Hierarchical Approach to Neural Context-Aware Modeling

We present a new recurrent neural network topology to enhance state-of-t...

Patrick Huber, et al. ∙

research

∙ 07/07/2018

Robust and Scalable Differentiable Neural Computer for Question Answering

Deep learning models are often not easily adaptable to new tasks and req...

Jörg Franke, et al. ∙

research

∙ 07/05/2018

Neural Language Codes for Multilingual Acoustic Models

Multilingual Speech Recognition is one of the most costly AI problems, b...

Markus Müller, et al. ∙

research

∙ 04/21/2018

Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation

We work on translation from rich-resource languages to low-resource lang...

Zhong Zhou, et al. ∙

research

∙ 03/26/2018

Self-Attentional Acoustic Models

Self-attention is a method of encoding sequences of vectors by relating ...

Matthias Sperber, et al. ∙

research

∙ 03/23/2018

Automated Evaluation of Out-of-Context Errors

We present a new approach to evaluate computational models for the task ...

Patrick Huber, et al. ∙

research

∙ 03/06/2018

An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation

Recently advancements in deep learning allowed the development of end-to...

Stefan Constantin, et al. ∙

research

∙ 12/19/2017

Subword and Crossword Units for CTC Acoustic Models

This paper proposes a novel approach to create an unit set for CTC based...

Thomas Zenkel, et al. ∙

research

∙ 11/13/2017

Multilingual Adaptation of RNN Based ASR Systems

A large amount of data is required for automatic speech recognition (ASR...

Markus Müller, et al. ∙

research

∙ 11/13/2017

Phonemic and Graphemic Multilingual CTC Based Speech Recognition

Training automatic speech recognition (ASR) systems requires large amoun...

Markus Müller, et al. ∙

research

∙ 09/15/2017

Transcribing Against Time

We investigate the problem of manually correcting errors from an automat...

Matthias Sperber, et al. ∙

research

∙ 08/15/2017

Comparison of Decoding Strategies for CTC Acoustic Models

Connectionist Temporal Classification has recently attracted a lot of in...

Thomas Zenkel, et al. ∙

research

∙ 08/02/2017

Analyzing Neural MT Search and Model Performance

In this paper, we offer an in-depth analysis about the modeling and sear...

Jan Niehues, et al. ∙

research

∙ 06/02/2017

Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor

Using supporting backchannel (BC) cues can make human-computer interacti...

Robin Ruede, et al. ∙

research

∙ 04/03/2017

Neural Lattice-to-Sequence Models for Uncertain Inputs

The input to a neural sequence-to-sequence model is often determined by ...

Matthias Sperber, et al. ∙

research

∙ 10/17/2016

Pre-Translation for Neural Machine Translation

Recently, the development of neural machine translation (NMT) has signif...

Jan Niehues, et al. ∙

research

∙ 04/28/2015

Lexical Translation Model Using a Deep Neural Network Architecture

In this paper we combine the advantages of a model using global source s...

Thanh-Le Ha, et al. ∙

Alex Waibel

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