This study evaluates the impact of speech enhancement (SE) techniques on linguistic research, focusing on their ability to maintain essential acoustic characteristics in enhanced audio without introducing significant artifacts. Through a sociophonetic analysis of Peninsular and Peruvian Spanish speakers, using both original and enhanced recordings, we demonstrate that SE effectively preserves critical speech nuances such as voicing and vowel quality. This supports the use of SE in improving the quality of speech samples. This study marks an initial effort to assess SE’s reliability in language studies and proposes a methodology for enhancing low-quality audio corpora of under-resourced languages.

Language models (LMs) have become pivotal in the realm of technological advancements. While their capabilities are vast and transformative, they often include societal biases encoded in the human-produced datasets used for their training. This research delves into the inherent biases present in masked language models (MLMs), with a specific focus on gender biases. This study evaluated six prominent models: BERT, RoBERTa, DistilBERT, BERT- multilingual, XLM-RoBERTa, and DistilBERT- multilingual. The methodology employed a novel dataset, bifurcated into two subsets: one containing prompts that encouraged models to generate subject pronouns in English and the other requiring models to return the probabilities of verbs, adverbs, and adjectives linked to the prompts’ gender pronouns. The analysis reveals stereotypical gender alignment of all models, with multilingual variants showing comparatively reduced biases.