Recognizing Zero-Resourced Languages Based on Mismatched Machine Transcriptions

Mismatched crowdsourcing based probabilistic human transcription has been proposed recently for training and adapting acoustic models for zero-resourced languages where we do not have any native transcriptions. This paper describes a machine transcription based phone recognition system for recognizing zero-resourced languages and compares it with baseline systems of MAP adaptation and semi-supervised self training. With a set of available speech recognizers in source languages that cover all the basic phonetic features, this work shows that we can use mismatched machine transcriptions from these source languages to achieve human level transcriptions, bypassing the laborious efforts of obtaining human transcriptions. We also present a fully automated unsupervised approach for zero-resourced speech recognition using mismatched machine transcriptions for transfer learning of phone models.