Automatic Speech Recognition Variance: Consecutive Runs of Low-Resource Languages in Whisper
International Journal of Machine Learning · 2024
This study employs OpenAI's Whisper to explore the manifestation of variance in an Automatic Speech Recognition (ASR) system.Three trained languages from Whisper's current offerings (English, French, and Haitian Kreyòl) and one untrained (Saint Lucian Kwéyòl) completed thirty consecutive runs each, across five model sizes.Etymologically complex yet orthographically simple, mutually intelligible languages may challenge ASR system capabilities.However, a phonetically similar trained language model generated approximate phonetic transcripts for an untrained one.Despite implicit variance hurdles like non-determinism and data deficiencies, ASR systems may aid in documenting high-orality, low-resource languages.