11 research outputs found
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. When the source sentence does not convey the
speaker's gender, speech translation (ST) models either rely on the
possibly-misleading vocal traits of the speaker or default to the masculine
gender, the most frequent in existing training corpora. To avoid such biased
and not inclusive behaviors, the gender assignment of speaker-related
expressions should be guided by externally-provided metadata about the
speaker's gender. While previous work has shown that the most effective
solution is represented by separate, dedicated gender-specific models, the goal
of this paper is to achieve the same results by integrating the speaker's
gender metadata into a single "multi-gender" neural ST model, easier to
maintain. Our experiments demonstrate that a single multi-gender model
outperforms gender-specialized ones when trained from scratch (with gender
accuracy gains up to 12.9 for feminine forms), while fine-tuning from existing
ST models does not lead to competitive results.Comment: To appear in CLiC-it 202
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. This can result in disparities in recognition accuracy between male and
female speakers, primarily due to the under-representation of the latter group
in the training data. While in the context of hybrid ASR models several
solutions have been proposed, the gender bias issue has not been explicitly
addressed in end-to-end neural architectures. To fill this gap, we propose a
data augmentation technique that manipulates the fundamental frequency (f0) and
formants. This technique reduces the data unbalance among genders by simulating
voices of the under-represented female speakers and increases the variability
within each gender group. Experiments on spontaneous English speech show that
our technique yields a relative WER improvement up to 9.87% for utterances by
female speakers, with larger gains for the least-represented f0 ranges.Comment: Accepted at ASRU 202
Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges
Gender inclusivity in language technologies has become a prominent research topic. In this study, we explore gender-neutral translation (GNT) as a form of gender inclusivity and a goal to be achieved by machine translation (MT) models, which have been found to perpetuate gender bias and discrimination. Specifically, we focus on translation from English into Italian, a language pair representative of salient gender-related linguistic transfer problems. To define GNT, we review a selection of relevant institutional guidelines for gender-inclusive language, discuss its scenarios of use, and examine the technical challenges of performing GNT in MT, concluding with a discussion of potential solutions to encourage advancements toward greater inclusivity in MT
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. As such, we first question the need of ASR pre-training, showing that it is not essential to achieve competitive results. Second, we focus on data filtering, showing that a simple method that looks at the ratio between source and target characters yields a quality improvement of 1 BLEU. Third, we compare different methods to reduce the detrimental effect of the audio segmentation mismatch between training data manually segmented at sentence level and inference data that is automatically segmented. Towards the same goal of training cost reduction, we participate in the simultaneous task with the same model trained for offline ST. The effectiveness of our lightweight training strategy is shown by the high score obtained on the MuST-C en-de corpus (26.7 BLEU) and is confirmed in high-resource data conditions by a 1.6 BLEU improvement on the IWSLT2020 test set over last year’s winning system
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. Rather, they should assign gender according to the speakers’ preference. The existing solutions to do so, though effective, are hardly feasible in practice as they involve dedicated model re-training on gender-labeled ST data. To overcome these limitations, we propose the first inference-time solution to control speaker-related gender inflections in ST. Our approach partially replaces the (biased) internal language model (LM) implicitly learned by the ST decoder with gender-specific external LMs. Experiments on en→es/fr/it show that our solution outperforms the base models and the best training-time mitigation strategy by up to 31.0 and 1.6 points in gender accuracy, respectively, for feminine forms. The gains are even larger (up to 32.0 and 3.4) in the challenging condition where speakers’ vocal traits conflict with their gender
CLUB Working Papers in Linguistics 4
Questo quarto volume della collana CLUB Working Papers in Linguistics raccoglie, in formato open access, una selezione dei contributi presentati durante l\u2019anno accademico 2018/2019 nell\u2019ambito delle attivit\ue0 del CLUB \u2013 Circolo Linguistico dell\u2019Universit\ue0 di Bologna.
Le diverse discipline del linguaggio che sono rappresentate in questa miscellanea offrono al lettore uno sguardo d\u2019insieme sulle ultime tendenze della ricerca in vari ambiti, tra cui la tipologia linguistica, la linguistica storica, la corpus linguistics, l\u2019analisi del discorso e la linguistica clinica.
Come nelle precedenti edizioni, viene qui pubblicato anche un saggio tratto dalla tesi di laurea magistrale risultata vincitrice del premio CLUB Day \u2018Una tesi in linguistica\u2019: quest\u2019anno, il lavoro scelto \ue8 di Dennis Fucci.
I sette articoli, redatti in italiano, inglese o francese, sono dunque (in ordine alfabetico) di Giorgio Francesco Arcodia, Alessandra Barotto, Claudia Roberta Combei, Claire Doquet, Chiara Fedriani, Dennis Fucci, Gloria Gagliardi e Simone Mattiola