Unsupervised training for acoustic models of speech - Donaj, Gregor; Žgank, Andrej; Sepesy Maučec, Mirjam (Anali PAZU, 2022)
This paper presents unsupervised acoustical model training for automatic speech recognition. The main advantage of this training method is its speed and cost effectiveness compared to the manual transcription of speech, which is needed for supervised training. We present two methods of unsupervised training and test them on a large vocabulary continuous speech recognition system in the Broadcast News domain.