Swedish Whispers; Leveraging a Massive Speech Corpus for Swedish Speech Recognition
This work provides improved speech recognition for Swedish speakers, but it is incremental as it fine-tunes existing models on new data.
The authors tackled the problem of underrepresentation of mid-resourced languages like Swedish in speech recognition by fine-tuning Whisper models on a large Swedish speech corpus, achieving an average 47% reduction in word error rate compared to OpenAI's whisper-large-v3.
This work presents a suite of fine-tuned Whisper models for Swedish, trained on a dataset of unprecedented size and variability for this mid-resourced language. As languages of smaller sizes are often underrepresented in multilingual training datasets, substantial improvements in performance can be achieved by fine-tuning existing multilingual models, as shown in this work. This work reports an overall improvement across model sizes compared to OpenAI's Whisper evaluated on Swedish. Most notably, we report an average 47% reduction in WER comparing our best performing model to OpenAI's whisper-large-v3, in evaluations across FLEURS, Common Voice, and NST.