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Found 42 papers, 11 papers with code
No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation
Automatic speech recognition (ASR) systems are known to be sensitive to the sociolinguistic variability of speech data, in which gender plays a crucial role.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
How To Build Competitive Multi-gender Speech Translation Models For Controlling Speaker Gender Translation
When translating from notional gender languages (e. g., English) into grammatical gender languages (e. g., Italian), the generated translation requires explicit gender assignments for various words, including those referring to the speaker.
Integrating Language Models into Direct Speech Translation: An Inference-Time Solution to Control Gender Inflection
When translating words referring to the speaker, speech translation (ST) systems should not resort to default masculine generics nor rely on potentially misleading vocal traits.
Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES
As part of the WMT-2023 "Test suites" shared task, in this paper we summarize the results of two test suites evaluations: MuST-SHE-WMT23 and INES.
How do Hyenas deal with Human Speech? Speech Recognition and Translation with ConfHyena
The attention mechanism, a cornerstone of state-of-the-art neural models, faces computational hurdles in processing long sequences due to its quadratic complexity.
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.
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.
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.
Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus
Gender inequality is embedded in our communication practices and perpetuated in translation technologies.
Neural versus Phrase-Based Machine Translation Quality: a Case Study
Within the field of Statistical Machine Translation (SMT), the neural approach (NMT) has recently emerged as the first technology able to challenge the long-standing dominance of phrase-based approaches (PBMT).
A SICK cure for the evaluation of compositional distributional semantic models
Shared and internationally recognized benchmarks are fundamental for the development of any computational system.
Chinese Whispers: Cooperative Paraphrase Acquisition
We present a framework for the acquisition of sentential paraphrases based on crowdsourcing.
The IWSLT 2011 Evaluation Campaign on Automatic Talk Translation
We report here on the eighth evaluation campaign organized in 2011 by the IWSLT workshop series.
MuST-C: a Multilingual Speech Translation Corpus
Current research on spoken language translation (SLT) has to confront with the scarcity of sizeable and publicly available training corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words
This paper presents WAGS (Word Alignment Gold Standard), a novel benchmark which allows extensive evaluation of WA tools on out-of-vocabulary (OOV) and rare words.
Machine Translation for Machines: the Sentiment Classification Use Case
Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback.
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus
Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines.
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.
Gender Bias in Machine Translation
Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, elaborating and communicating information.
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.
CEF Data Marketplace: Powering a Long-term Supply of Language Data
We describe the CEF Data Marketplace project, which focuses on the development of a trading platform of translation data for language professionals: translators, machine translation (MT) developers, language service providers (LSPs), translation buyers and government bodies.
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.
Machine Translation Human Evaluation: an investigation of evaluation based on Post-Editing and its relation with Direct Assessment
In this paper we present an analysis of the two most prominent methodologies used for the human evaluation of MT quality, namely evaluation based on Post-Editing (PE) and evaluation based on Direct Assessment (DA).
The IWSLT 2016 Evaluation Campaign
The IWSLT 2016 Evaluation Campaign featured two tasks: the translation of talks and the translation of video conference conversations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Overview of the IWSLT 2017 Evaluation Campaign
The IWSLT 2017 evaluation campaign has organised three tasks.
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.
Post-editing in Automatic Subtitling: A Subtitlers’ perspective
Subtitling tools are recently being adapted for post-editing by providing automatically generated subtitles, and featuring not only machine translation, but also automatic segmentation and synchronisation.
Towards a methodology for evaluating automatic subtitling
In response to the increasing interest towards automatic subtitling, this EAMT-funded project aimed at collecting subtitle post-editing data in a real use case scenario where professional subtitlers edit automatically generated subtitles.
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).
Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges
Gender inclusivity in language technologies has become a prominent research topic.
Good, but not always Fair: An Evaluation of Gender Bias for three commercial Machine Translation Systems
Machine Translation (MT) continues to make significant strides in quality and is increasingly adopted on a larger scale.
A Prompt Response to the Demand for Automatic Gender-Neutral Translation
Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies.
Speech Translation with Speech Foundation Models and Large Language Models: What is There and What is Missing?
The field of natural language processing (NLP) has recently witnessed a transformative shift with the emergence of foundation models, particularly Large Language Models (LLMs) that have revolutionized text-based NLP.
