Unsupervised speech transcription and alignment based on two complementary ASR systems

Abstract

An acoustic model is a necessary component of automatic speech recognition system. Acoustic models are trained on a lot of speech recordings with transcriptions. Usually, hundreds of transcribed recordings are required. It is very time and resource consuming process to create manual transcriptions. Acoustic models may be obtained automatically with unsupervised acoustic model training, which uses online speech resources. Obtained speech data are recognized with low resourced automatic speech recognition system. Unsupervised techniques are able to filter out the erroneous hypotheses from the result and the rest use for acoustic model training. Unsupervised methods for generating speech corpora for acoustic model training are presented in this paper.