Investigating the Impact of Gender Representation in ASR Training Data: a Case Study on Librispeech
In this paper we question the impact of gender representation in training data on the performance of an end-to-end ASR system. We create an experiment based on the Librispeech corpus and build 3 different training corpora varying only the proportion of data produced by each gender category. We observe that if our system is overall robust to the gender balance or imbalance in training data, it is nonetheless dependant of the adequacy between the individuals present in the training and testing sets.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Investigating gender adaptation for speech translation
In this paper we investigate the impact of the integration of context into dialogue translation. We present a new contextual parallel corpus of television subtitles and show how taking into account speaker gender can sig…
Machine TranslationTranslationInvestigating Gender Stereotypes in Large Language Models via Social Determinants of Health
Large Language Models (LLMs) excel in Natural Language Processing (NLP) tasks, but they often propagate biases embedded in their training data, which is potentially impactful in sensitive domains like healthcare. While e…
Investigating the Roots of Gender Bias in Machine Translation: Observations on Gender Transfer between French and English
This paper aims at identifying the inner mechanisms that make a translation model choose a masculine rather than a feminine form, an essential step to mitigate gender bias in MT. We conduct two series of experiments u…
Machine TranslationTranslationInvestigating Bias Representations in Llama 2 Chat via Activation Steering
We address the challenge of societal bias in Large Language Models (LLMs), focusing on the Llama 2 7B Chat model. As LLMs are increasingly integrated into decision-making processes with substantial societal impact, it be…
Decision MakingRed TeamingGrep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results
The provided contents by information retrieval (IR) systems can reflect the existing societal biases and stereotypes. Such biases in retrieval results can lead to further establishing and strengthening stereotypes in soc…
Information RetrievalRetrieval