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Super-Human Performance in Online Low-latency Recognition of Conversational Speech
Achieving super-human performance in recognizing human speech has been a...
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Error-correction and extraction in request dialogs
We propose a component that gets a request and a correction and outputs ...
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High Performance Sequence-to-Sequence Model for Streaming Speech Recognition
Recently sequence-to-sequence models have started to achieve state-of-th...
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Low Latency ASR for Simultaneous Speech Translation
User studies have shown that reducing the latency of our simultaneous le...
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Toward Cross-Domain Speech Recognition with End-to-End Models
In the area of multi-domain speech recognition, research in the past foc...
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An Interactive Indoor Drone Assistant
With the rapid advance of sophisticated control algorithms, the capabili...
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Bimodal Speech Emotion Recognition Using Pre-Trained Language Models
Speech emotion recognition is a challenging task and an important step t...
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Low-Resource Machine Translation using Interlinear Glosses
Neural Machine Translation (NMT) does not handle low-resource translatio...
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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-...
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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...
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Improving Zero-shot Translation with Language-Independent Constraints
An important concern in training multilingual neural machine translation...
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Self-Attentional Models for Lattice Inputs
Lattices are an efficient and effective method to encode ambiguity of up...
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Fluent Translations from Disfluent Speech in End-to-End Speech Translation
Spoken language translation applications for speech suffer due to conver...
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Very Deep Self-Attention Networks for End-to-End Speech Recognition
Recently, end-to-end sequence-to-sequence models for speech recognition ...
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Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
Speech translation has traditionally been approached through cascaded mo...
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Learning Shared Encoding Representation for End-to-End Speech Recognition Models
In this work, we learn a shared encoding representation for a multi-task...
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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...
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Multi-task learning to improve natural language understanding
Recently advancements in sequence-to-sequence neural network architectur...
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Towards Fluent Translations from Disfluent Speech
When translating from speech, special consideration for conversational s...
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Towards one-shot learning for rare-word translation with external experts
Neural machine translation (NMT) has significantly improved the quality ...
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Paraphrases as Foreign Languages in Multilingual Neural Machine Translation
Using paraphrases, the expression of the same semantic meaning in differ...
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Low-Latency Neural Speech Translation
Through the development of neural machine translation, the quality of ma...
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A Hierarchical Approach to Neural Context-Aware Modeling
We present a new recurrent neural network topology to enhance state-of-t...
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Robust and Scalable Differentiable Neural Computer for Question Answering
Deep learning models are often not easily adaptable to new tasks and req...
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Neural Language Codes for Multilingual Acoustic Models
Multilingual Speech Recognition is one of the most costly AI problems, b...
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Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation
We work on translation from rich-resource languages to low-resource lang...
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Self-Attentional Acoustic Models
Self-attention is a method of encoding sequences of vectors by relating ...
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Automated Evaluation of Out-of-Context Errors
We present a new approach to evaluate computational models for the task ...
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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...
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Subword and Crossword Units for CTC Acoustic Models
This paper proposes a novel approach to create an unit set for CTC based...
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Multilingual Adaptation of RNN Based ASR Systems
A large amount of data is required for automatic speech recognition (ASR...
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Phonemic and Graphemic Multilingual CTC Based Speech Recognition
Training automatic speech recognition (ASR) systems requires large amoun...
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Transcribing Against Time
We investigate the problem of manually correcting errors from an automat...
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Comparison of Decoding Strategies for CTC Acoustic Models
Connectionist Temporal Classification has recently attracted a lot of in...
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Analyzing Neural MT Search and Model Performance
In this paper, we offer an in-depth analysis about the modeling and sear...
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Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor
Using supporting backchannel (BC) cues can make human-computer interacti...
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Neural Lattice-to-Sequence Models for Uncertain Inputs
The input to a neural sequence-to-sequence model is often determined by ...
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Pre-Translation for Neural Machine Translation
Recently, the development of neural machine translation (NMT) has signif...
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Lexical Translation Model Using a Deep Neural Network Architecture
In this paper we combine the advantages of a model using global source s...
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