Search Results for author:
Found 52 papers, 20 papers with code
Findings of the WMT 2020 Shared Task on Machine Translation Robustness
We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT).
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.
FINDINGS OF THE IWSLT 2021 EVALUATION CAMPAIGN
The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2021) featured this year four shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Multilingual speech translation, (iv) Low-resource speech translation.
fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit
This paper presents fairseq Sˆ2, a fairseq extension for speech synthesis.
SeamlessM4T-Massively Multilingual & Multimodal Machine Translation
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages?
Automatic Speech Recognition
Speech-to-Speech Translation
+3
Multilingual Speech-to-Speech Translation into Multiple Target Languages
Despite a few studies on multilingual S2ST, their focus is the multilinguality on the source side, i. e., the translation from multiple source languages to one target language.
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks
In order to leverage strengths of both modeling methods, we propose a solution by combining Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Enhancing Speech-to-Speech Translation with Multiple TTS Targets
It has been known that direct speech-to-speech translation (S2ST) models usually suffer from the data scarcity issue because of the limited existing parallel materials for both source and target speech.
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community.
MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text Translation
We introduce MuAViC, a multilingual audio-visual corpus for robust speech recognition and robust speech-to-text translation providing 1200 hours of audio-visual speech in 9 languages.
Audio-Visual Speech Recognition
Robust Speech Recognition
+4
Pre-training for Speech Translation: CTC Meets Optimal Transport
Nevertheless, CTC is only a partial solution and thus, in our second contribution, we propose a novel pre-training method combining CTC and optimal transport to further reduce this gap.
UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units
We enhance the model performance by subword prediction in the first-pass decoder, advanced two-pass decoder architecture design and search strategy, and better training regularization.
Speech-to-Speech Translation For A Real-world Unwritten Language
We use English-Taiwanese Hokkien as a case study, and present an end-to-end solution from training data collection, modeling choices to benchmark dataset release.
SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations
We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings.
Simple and Effective Unsupervised Speech Translation
The amount of labeled data to train models for speech tasks is limited for most languages, however, the data scarcity is exacerbated for speech translation which requires labeled data covering two different languages.
Unified Speech-Text Pre-training for Speech Translation and Recognition
Two pre-training configurations for speech translation and recognition, respectively, are presented to alleviate subtask interference.
Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation
Direct speech-to-speech translation (S2ST) models suffer from data scarcity issues as there exists little parallel S2ST data, compared to the amount of data available for conventional cascaded systems that consist of automatic speech recognition (ASR), machine translation (MT), and text-to-speech (TTS) synthesis.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Textless Speech-to-Speech Translation on Real Data
To our knowledge, we are the first to establish a textless S2ST technique that can be trained with real-world data and works for multiple language pairs.
XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
On the CoVoST-2 speech translation benchmark, we improve the previous state of the art by an average of 7. 4 BLEU over 21 translation directions into English.
Ranked #1 on
Language Identification
on VoxLingua107
(using extra training data)
Direct Simultaneous Speech-to-Speech Translation with Variational Monotonic Multihead Attention
We present a direct simultaneous speech-to-speech translation (Simul-S2ST) model, Furthermore, the generation of translation is independent from intermediate text representations.
From Start to Finish: Latency Reduction Strategies for Incremental Speech Synthesis in Simultaneous Speech-to-Speech Translation
Speech-to-speech translation (S2ST) converts input speech to speech in another language.
fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit
This paper presents fairseq S^2, a fairseq extension for speech synthesis.
Multilingual Speech Translation from Efficient Finetuning of Pretrained Models
We present a simple yet effective approach to build multilingual speech-to-text (ST) translation through efficient transfer learning from a pretrained speech encoder and text decoder.
FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task
In this paper, we describe our end-to-end multilingual speech translation system submitted to the IWSLT 2021 evaluation campaign on the Multilingual Speech Translation shared task.
Direct speech-to-speech translation with discrete units
When target text transcripts are available, we design a joint speech and text training framework that enables the model to generate dual modality output (speech and text) simultaneously in the same inference pass.
Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task
Pretraining and multitask learning are widely used to improve the speech to text translation performance.
Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling
We further propose attention sharing strategies to facilitate parameter sharing and specialization in multilingual and multi-domain sequence modeling.
Lightweight Adapter Tuning for Multilingual Speech Translation
Adapter modules were recently introduced as an efficient alternative to fine-tuning in NLP.
Ranked #1 on
Speech-to-Text Translation
on MuST-C EN->ES
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Large-Scale Self- and Semi-Supervised Learning for Speech Translation
In this paper, we improve speech translation (ST) through effectively leveraging large quantities of unlabeled speech and text data in different and complementary ways.
VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation
We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages.
Ranked #3 on
Speech Recognition
on Common Voice French
(using extra training data)
SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation
Simultaneous text translation and end-to-end speech translation have recently made great progress but little work has combined these tasks together.
Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation
We propose two variants of these architectures corresponding to two different levels of dependencies between the decoders, called the parallel and cross dual-decoder Transformers, respectively.
Ranked #1 on
Speech-to-Text Translation
on MuST-C EN->FR
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Streaming Simultaneous Speech Translation with Augmented Memory Transformer
Transformer-based models have achieved state-of-the-art performance on speech translation tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multilingual Speech Translation with Efficient Finetuning of Pretrained Models
We present a simple yet effective approach to build multilingual speech-to-text (ST) translation by efficient transfer learning from pretrained speech encoder and text decoder.
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks
We demonstrate that representing text input as phoneme sequences can reduce the difference between speech and text inputs, and enhance the knowledge transfer from text corpora to the speech to text tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
fairseq S2T: Fast Speech-to-Text Modeling with fairseq
We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation.
Ranked #8 on
Speech-to-Text Translation
on MuST-C EN->DE
SimulEval: An Evaluation Toolkit for Simultaneous Translation
We also adapt latency metrics from text simultaneous translation to the speech task.
CoVoST 2 and Massively Multilingual Speech-to-Text Translation
Speech translation has recently become an increasingly popular topic of research, partly due to the development of benchmark datasets.
FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN
The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation.
Self-Supervised Representations Improve End-to-End Speech Translation
End-to-end speech-to-text translation can provide a simpler and smaller system but is facing the challenge of data scarcity.
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation
Even with pseudo-labels from low-resource MT (200K examples), ST-enhanced transfer brings up to 8. 9% WER reduction to direct transfer.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Self-Training for End-to-End Speech Translation
One of the main challenges for end-to-end speech translation is data scarcity.
SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech Translation
Our method compares favorably to SpecAugment on English$\to$French and English$\to$Romanian automatic speech translation (AST) tasks as well as on a low-resource English automatic speech recognition (ASR) task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus
Spoken language translation has recently witnessed a resurgence in popularity, thanks to the development of end-to-end models and the creation of new corpora, such as Augmented LibriSpeech and MuST-C.
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali--English and Sinhala--English
For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available.
Monotonic Multihead Attention
Simultaneous machine translation models start generating a target sequence before they have encoded or read the source sequence.
Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade
In this work, we evaluate several data augmentation and pretraining approaches for AST, by comparing all on the same datasets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions
Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2{\%} and 10{\%} of the highest-quality data to be used to train machine translation systems.
Findings of the First Shared Task on Machine Translation Robustness
We share the findings of the first shared task on improving robustness of Machine Translation (MT).
The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English
For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available.
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
The application of deep learning techniques resulted in remarkable improvement of machine learning models.
