Current evaluation practices in speech processing systems often overlook the diversity of spoken accents, leading to significant performance disparities across speaker groups. This issue largely comes from biases and imbalances in training corpora, and is further compounded by the scarcity of open-source datasets suitable for evaluating accent variability in French. To address this gap, we extend the CFPR dataset with explicit accent labels, providing a new benchmark for assessing the robustness of speech technology systems across diverse French accents. We additionally conduct a perceptual study with 87 human participants to evaluate the reliability and interpretability of these labels. Using this resource, we evaluated an eight-class French accent classifier trained on Common Voice data. The first results highlight both the complexity of automatic French accent recognition in low-resource settings, and the difficulty for French-speakers to perceive all the linguistic variabilities in French-speaking countries.

Dans cette démonstration, nous présentons l’annotateur multi-niveaux DisMo, un outil conçu pour faire face aux spécificités des corpus oraux. Il fournit une annotation morphosyntaxique, une lemmatisation, une détection des unités poly-lexicales, une détection des phénomènes de disfluence et des marqueurs de discours.

We present DisMo, a multi-level annotator for spoken language corpora that integrates part-of-speech tagging with basic disfluency detection and annotation, and multi-word unit recognition. DisMo is a hybrid system that uses a combination of lexical resources, rules, and statistical models based on Conditional Random Fields (CRF). In this paper, we present the first public version of DisMo for French. The system is trained and its performance evaluated on a 57k-token corpus, including different varieties of French spoken in three countries (Belgium, France and Switzerland). DisMo supports a multi-level annotation scheme, in which the tokenisation to minimal word units is complemented with multi-word unit groupings (each having associated POS tags), as well as separate levels for annotating disfluencies and discourse phenomena. We present the system’s architecture, linguistic resources and its hierarchical tag-set. Results show that DisMo achieves a precision of 95% (finest tag-set) to 96.8% (coarse tag-set) in POS-tagging non-punctuated, sound-aligned transcriptions of spoken French, while also offering substantial possibilities for automated multi-level annotation.