Lightly supervised acoustic model training for imprecisely and asynchronously transcribed speech

In a variety of speech recognition tasks a large amount of approximate transcription is available for the audio material, but is not directly applicable for acoustic model training. Whereas roughly time-synchronous closed-captions or proper audiobook texts are already used in lightly supervised techniques, the utilization of more imperfect and at the same time completely unaligned transcriptions is not self-evident. In this paper we describe our experiments aiming at automated transcription of Hungarian parliamentary speeches. Essentially, a lightly supervised across-domain acoustic model adaptation/retraining is performed. A low-resource broadcast news model is used to bootstrap the process. Relying on automatic recognition of parliamentary training speech and on dynamic text alignment based data selection, a new, task-specific acoustic model is built. For the adaptation to the parliamentary domain, only edited official transcriptions and unaligned speech data are used, without any additional human annotation effort. The adapted acoustic model is applied on unseen target speech in real-time recognition. The word accuracy difference between the automatic and the human powered, official transcription is only 5% (as compared to the exact reference text).