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Found 37 papers, 6 papers with code
Toward Robust Neural Machine Translation for Noisy Input Sequences
We note that unlike our baseline model, models trained on noisy data are able to generate outputs of proper length even for noisy inputs, while gradually reducing output length for higher amount of noise, as might also be expected from a human translator.
The 2017 KIT IWSLT Speech-to-Text Systems for English and German
For the English lecture task, our best combination system has a WER of 8. 3% on the tst2015 development set while our other combinations gained 25. 7% WER for German lecture tasks.
KIT’s Multilingual Neural Machine Translation systems for IWSLT 2017
For the SLT track, in addition to a monolingual neural translation system used to generate correct punctuations and true cases of the data prior to training our multilingual system, we introduced a noise model in order to make our system more robust.
Audio Segmentation for Robust Real-Time Speech Recognition Based on Neural Networks
Speech that contains multimedia content can pose a serious challenge for real-time automatic speech recognition (ASR) for two reasons: (1) The ASR produces meaningless output, hurting the readability of the transcript.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Findings of the IWSLT 2022 Evaluation Campaign
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
The 2016 KIT IWSLT Speech-to-Text Systems for English and German
For the English TED task, our best combination system has a WER of 7. 8% on the development set while our other combinations gained 21. 8% and 28. 7% WERs for the English and German MSLT tasks.
Adaptation and Combination of NMT Systems: The KIT Translation Systems for IWSLT 2016
In addition, we investigated methods to combine NMT systems that encode the input as well as the output differently.
KIT’s IWSLT 2018 SLT Translation System
The baseline system is a cascade of an ASR system, a system to segment the ASR output and a neural machine translation system.
Towards Real-World Streaming Speech Translation for Code-Switched Speech
Code-switching (CS), i. e. mixing different languages in a single sentence, is a common phenomenon in communication and can be challenging in many Natural Language Processing (NLP) settings.
Automating Behavioral Testing in Machine Translation
Behavioral testing in NLP allows fine-grained evaluation of systems by examining their linguistic capabilities through the analysis of input-output behavior.
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data
Self-training has been shown to be helpful in addressing data scarcity for many domains, including vision, speech, and language.
End-to-End Speech Translation for Code Switched Speech
Code switching (CS) refers to the phenomenon of interchangeably using words and phrases from different languages.
Streaming Models for Joint Speech Recognition and Translation
However, all previous work has only looked at this problem from the consecutive perspective, leaving uncertainty on whether these approaches are effective in the more challenging streaming setting.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Consistent Transcription and Translation of Speech
To address various shortcomings of this paradigm, recent work explores end-to-end trainable direct models that translate without transcribing.
Variational Neural Machine Translation with Normalizing Flows
Variational Neural Machine Translation (VNMT) is an attractive framework for modeling the generation of target translations, conditioned not only on the source sentence but also on some latent random variables.
Speech Translation and the End-to-End Promise: Taking Stock of Where We Are
Over its three decade history, speech translation has experienced several shifts in its primary research themes; moving from loosely coupled cascades of speech recognition and machine translation, to exploring questions of tight coupling, and finally to end-to-end models that have recently attracted much attention.
Low Latency ASR for Simultaneous Speech Translation
User studies have shown that reducing the latency of our simultaneous lecture translation system should be the most important goal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation
Previous work on end-to-end translation from speech has primarily used frame-level features as speech representations, which creates longer, sparser sequences than text.
Self-Attentional Models for Lattice Inputs
Lattices are an efficient and effective method to encode ambiguity of upstream systems in natural language processing tasks, for example to compactly capture multiple speech recognition hypotheses, or to represent multiple linguistic analyses.
Fluent Translations from Disfluent Speech in End-to-End Speech Translation
Spoken language translation applications for speech suffer due to conversational speech phenomena, particularly the presence of disfluencies.
Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
Speech translation has traditionally been approached through cascaded models consisting of a speech recognizer trained on a corpus of transcribed speech, and a machine translation system trained on parallel texts.
Paraphrases as Foreign Languages in Multilingual Neural Machine Translation
Our multi-paraphrase NMT that trains only on two languages outperforms the multilingual baselines.
Low-Latency Neural Speech Translation
After adaptation, we are able to reduce the number of corrections displayed during incremental output construction by 45%, without a decrease in translation quality.
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning
{\%} Combining these techniques, we are able to provide an adapted speech translation system for several European languages.
Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation
The main challenges we identify are the lack of low-resource language data, effective methods for cross-lingual transfer, and the variable-binding problem that is common in neural systems.
Self-Attentional Acoustic Models
Self-attention is a method of encoding sequences of vectors by relating these vectors to each-other based on pairwise similarities.
XNMT: The eXtensible Neural Machine Translation Toolkit
In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing.
Transcribing Against Time
We investigate the problem of manually correcting errors from an automatic speech transcript in a cost-sensitive fashion.
Comparison of Decoding Strategies for CTC Acoustic Models
The CTC loss function maps an input sequence of observable feature vectors to an output sequence of symbols.
Neural Lattice-to-Sequence Models for Uncertain Inputs
In this work, we extend the TreeLSTM (Tai et al., 2015) into a LatticeLSTM that is able to consume word lattices, and can be used as encoder in an attentional encoder-decoder model.
Lightly Supervised Quality Estimation
Evaluating the quality of output from language processing systems such as machine translation or speech recognition is an essential step in ensuring that they are sufficient for practical use.
Optimizing Computer-Assisted Transcription Quality with Iterative User Interfaces
Our goal is to improve the human transcription quality via appropriate user interface design.
Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff
In this paper, we study the problem of manually correcting automatic annotations of natural language in as efficient a manner as possible.
