Many studies have shown automatic speech processing (ASR) systems have unequal performance across speaker groups (SG’s). However, the manner in which such studies arrive at this conclusion is inconsistent. To pave the way for more reliable results in future studies, we lay out best practices for benchmarking ASR fairness based on literature from machine learning fairness, social sciences, and speech science. We then perform a case study on the Fair-speech benchmark, applying aforementioned best practices, and discuss how failing to do so can result in erroneous conclusions. On the whole, we advocate for as fine-grained an analysis as possible, taking into account as many variables as are available, in order to eschew dataset-level bias.

Pretrained Language Models (PLMs) are the de facto backbone of most state-of-the-art NLP systems. In this paper, we introduce a family of domain-specific pretrained PLMs for French, focusing on three important domains: transcribed speech, medicine, and law. We use a transformer architecture based on efficient methods (LinFormer) to maximise their utility, since these domains often involve processing long documents. We evaluate and compare our models to state-of-the-art models on a diverse set of tasks and datasets, some of which are introduced in this paper. We gather the datasets into a new French-language evaluation benchmark for these three domains. We also compare various training configurations: continued pretraining, pretraining from scratch, as well as single- and multi-domain pretraining. Extensive domain-specific experiments show that it is possible to attain competitive downstream performance even when pre-training with the approximative LinFormer attention mechanism. For full reproducibility, we release the models and pretraining data, as well as contributed datasets.