@inproceedings{fily-etal-2026-investigating,
    title = "Investigating Speaker Pronunciation Variability in Speech Embeddings: Speaker and {L}1 Effects on {F}rench as a Second Language",
    author = "Fily, Maxime  and
      Adda-Decker, Martine  and
      Wisniewski, Guillaume",
    editor = "Hosseini-Kivanani, Nina  and
      Brutti, Alessio  and
      Matassoni, Marco  and
      Dowerah, Sandipana  and
      Liga, Davide  and
      Schommer, Christoph",
    booktitle = "Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis ({SPEAKABLE}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-previews/2026.speakable-1.10/",
    doi = "10.63317/4abhhjss97b8",
    pages = "86--97",
    abstract = "Speech variation between native and non-native speakers of French is addressed with a low-resource method based on a frame-wise comparison of wav2vec2 acoustic embeddings, using fine-grained phonetic transcriptions by expert annotators as baseline. z-normalisation and t-normalisation are explored to assess what the embeddings contain in terms of phonetically analysable information. We explore non-supervised methods for solving basic speech-related research questions. Adapting Dynamic Time Warping to speech embeddings, we compare phonologically similar recordings of sentences read-aloud by native vs. non-native speakers of French. The question is whether XLSR-53 embeddings are more robust than MFCCs to inter-speaker vs. intra-speaker variability for same words. Then we investigate whether native speaker productions are more stable than those of non-native speakers. Results suggest that the model allows phonetically meaningful correlative analyses. Working on the raw embeddings shows however that the representations are not speaker-independent, so with a view to address issues in relationship with L2 pronunciation variability, we show that t-normalisation brings us a way to separate fluency and accuracy effects in L2-speech. This shows that wav2vec2 encapsulates time-dependent phonetic information in the embeddings, including speaker accent which can not easily be disentangled from speaker ID."
}