This study presents our attempt on applying Query by Example - Spoken Term Detection methodologies to a real-world, low-resource scenario: building an audio-based query functionality for the diasporan Teochew dictionary WhatTCSay. This functionality enables users to retrieve dictionary entries without prior knowledge of the writing systems in Teochew, thereby enhancing the accessibility of the dictionary and facilitating language revitalization efforts within Teochew communities. To address the retrieval task, we investigate two approaches: (i) an ASR-based approach using text-to-text matching, and (ii) a Dynamic Time Warping (DTW)-based acoustic framework for audio-to-audio retrieval. In the first approach, we compare an automatic romanization of the spoken query against the gold romanization from the dictionary; in the second, we directly match the user’s spoken query against audio recordings from the dictionary pronounced by a native speaker. Retrieval performance is evaluated using recall at rank k. Results show that text-to-text matching achieves better performance than audio-to-audio matching; however, the two approaches were not optimized under fully comparable conditions, as the ASR-based approach benefited from additional optimization, which was not equally available for the DTW method.

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.

Despite decades of progress in human language technology (HLT) and growing research interest in endangered languages, practical uptake of HLT in documentary linguistics workflows remains rare. In this opinion piece, we report on a structured dialogue among approximately twenty academics convened to diagnose why this gap persists. Across all topics, we identify a recurring structural problem, which we call the missing middle: despite the existence of many potentially useful HLTs, the connective infrastructure necessary to make them genuinely accessible to linguists and language communities does not exist. We report the details of our discussion and make four specific recommendations for how those active in language documentation and HLT research might orient their future work.

Nous explorons les représentations vectorielles de la parole à partir d’un modèle pré-entraîné pour déterminer leur niveau d’abstraction par rapport au signal audio. Nous proposons une nouvelle méthode non-supervisée exploitant des données audio ayant des métadonnées soigneusement organisées pour apporter un éclairage sur les informations présentes dans les représentations. Des tests ABX déterminent si les représentations obtenues via un modèle de parole multilingue encodent une caractéristique donnée. Trois expériences sont présentées, portant sur la qualité acoustique de la pièce, le type de discours, ou le contenu phonétique. Les résultats confirment que les différences au niveau de caractéristiques linguistiques/extra-linguistiques d’enregistrements audio sont reflétées dans les représentations de ceux-ci. Plus la quantité d’audio par vecteur est importante, mieux elle permet de distinguer les caractéristiques extra-linguistiques. Plus elle est faible, et mieux nous pouvons distinguer les informations d’ordre phonétique/segmental. La méthode proposée ouvre de nouvelles pistes pour la recherche et les travaux comparatifs sur les langues peu dotées.

In the highly constrained context of low-resource language studies, we explore vector representations of speech from a pretrained model to determine their level of abstraction with regard to the audio signal. We propose a new unsupervised method using ABX tests on audio recordings with carefully curated metadata to shed light on the type of information present in the representations. ABX tests determine whether the representations computed by a multilingual speech model encode a given characteristic. Three experiments are devised: one on room acoustics aspects, one on linguistic genre, and one on phonetic aspects. The results confirm that the representations extracted from recordings with different linguistic/extra-linguistic characteristics differ along the same lines. Embedding more audio signal in one vector better discriminates extra-linguistic characteristics, whereas shorter snippets are better to distinguish segmental information. The method is fully unsupervised, potentially opening new research avenues for comparative work on under-documented languages.

This is a report on results obtained in the development of speech recognition tools intended to support linguistic documentation efforts. The test case is an extensive fieldwork corpus of Japhug, an endangered language of the Trans-Himalayan (Sino-Tibetan) family. The goal is to reduce the transcription workload of field linguists. The method used is a deep learning approach based on the language-specific tuning of a generic pre-trained representation model, XLS-R, using a Transformer architecture. We note difficulties in implementation, in terms of learning stability. But this approach brings significant improvements nonetheless. The quality of phonemic transcription is improved over earlier experiments; and most significantly, the new approach allows for reaching the stage of automatic word recognition. Subjective evaluation of the tool by the author of the training data confirms the usefulness of this approach.