From 2.1M Hours of Raw Broadcast Audio to Finnish Speech SSL: Confidence Filtering and Architectural Analysis at Scale
Read the original on arXiv Computation and Language →The paper investigates how to improve self‑supervised learning (SSL) for Finnish speech by filtering broadcast audio based on automatic speech recognition (ASR) confidence rather than traditional voice activity detection or language identification. Using 2.1 million hours of raw broadcast audio, the authors demonstrate that ASR‑confidence filtering yields 3–7 % relative WER gains over LID filtering while training on a smaller dataset. They also conduct a controlled comparison of four SSL architectures—Transformer, Macaron‑Net, Conformer, and E‑Branchformer—at matched parameter budgets, showing that their models surpass previous Finnish SSL baselines, especially at Base scale and on out‑of‑domain data.
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