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Found 22 papers, 11 papers with code
Is “moby dick” a Whale or a Bird? Named Entities and Terminology in Speech Translation
Automatic translation systems are known to struggle with rare words.
Extending the MuST-C Corpus for a Comparative Evaluation of Speech Translation Technology
This project aimed at extending the test sets of the MuST-C speech translation (ST) corpus with new reference translations.
On the Dynamics of Gender Learning in Speech Translation
In this work, we contribute to such a line of inquiry by exploring the emergence of gender bias in Speech Translation (ST).
Over-Generation Cannot Be Rewarded: Length-Adaptive Average Lagging for Simultaneous Speech Translation
Simultaneous speech translation (SimulST) systems aim at generating their output with the lowest possible latency, which is normally computed in terms of Average Lagging (AL).
Who Are We Talking About? Handling Person Names in Speech Translation
Recent work has shown that systems for speech translation (ST) -- similarly to automatic speech recognition (ASR) -- poorly handle person names.
Efficient yet Competitive Speech Translation: FBK@IWSLT2022
The primary goal of this FBK's systems submission to the IWSLT 2022 offline and simultaneous speech translation tasks is to reduce model training costs without sacrificing translation quality.
Does Simultaneous Speech Translation need Simultaneous Models?
In simultaneous speech translation (SimulST), finding the best trade-off between high translation quality and low latency is a challenging task.
Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation
Gender bias is largely recognized as a problematic phenomenon affecting language technologies, with recent studies underscoring that it might surface differently across languages.
Is "moby dick" a Whale or a Bird? Named Entities and Terminology in Speech Translation
Automatic translation systems are known to struggle with rare words.
Speechformer: Reducing Information Loss in Direct Speech Translation
Transformer-based models have gained increasing popularity achieving state-of-the-art performance in many research fields including speech translation.
Ranked #1 on
Speech-to-Text Translation
on MuST-C EN->NL
Between Flexibility and Consistency: Joint Generation of Captions and Subtitles
Speech translation (ST) has lately received growing interest for the generation of subtitles without the need for an intermediate source language transcription and timing (i. e. captions).
Dealing with training and test segmentation mismatch: FBK@IWSLT2021
Both knowledge distillation and the first fine-tuning step are carried out on manually segmented real and synthetic data, the latter being generated with an MT system trained on the available corpora.
Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?
Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions.
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation
In light of this finding, we propose a combined approach that preserves BPE overall translation quality, while leveraging the higher ability of character-based segmentation to properly translate gender.
Beyond Voice Activity Detection: Hybrid Audio Segmentation for Direct Speech Translation
The audio segmentation mismatch between training data and those seen at run-time is a major problem in direct speech translation.
Gender Bias in Machine Translation
Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, elaborating and communicating information.
CTC-based Compression for Direct Speech Translation
Previous studies demonstrated that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST).
Breeding Gender-aware Direct Speech Translation Systems
In particular, by translating speech audio data without intermediate transcription, direct ST models are able to leverage and preserve essential information present in the input (e. g. speaker's vocal characteristics) that is otherwise lost in the cascade framework.
On Knowledge Distillation for Direct Speech Translation
Direct speech translation (ST) has shown to be a complex task requiring knowledge transfer from its sub-tasks: automatic speech recognition (ASR) and machine translation (MT).
On Target Segmentation for Direct Speech Translation
Then, subword-level segmentation became the state of the art in neural machine translation as it produces shorter sequences that reduce the training time, while being superior to word-level models.
Contextualized Translation of Automatically Segmented Speech
We show that our context-aware solution is more robust to VAD-segmented input, outperforming a strong base model and the fine-tuning on different VAD segmentations of an English-German test set by up to 4. 25 BLEU points.
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020
The test talks are provided in two versions: one contains the data already segmented with automatic tools and the other is the raw data without any segmentation.
