Search Results for author:
Found 8 papers, 6 papers with code
Multilingual Speech Models for Automatic Speech Recognition Exhibit Gender Performance Gaps
However, the advantaged group varies between languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
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.
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.
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
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.
Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges
Gender inclusivity in language technologies has become a prominent research topic.
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.
