Unsupervised training for mandarin broadcast news and conversation transcription

A significant cost in obtaining acoustic training data is the generation of accurate transcriptions. For some sources close-caption data is available. This allows the use of lightly-supervised training techniques. However, for some sources and languages close-caption is not available. In these cases unsupervised training techniques must be used. This paper examines the use of unsupervised techniques for discriminative training. In unsupervised training automatic transcriptions from a recognition system are used for training. As these transcriptions may be errorful data selection may be useful. Two forms of selection are described, one to remove non-target language shows, the other to remove segments with low confidence. Experiments were carried out on a Mandarin transcriptions task. Two types of test data were considered, Broadcast News (BN) and Broadcast Conversations (BC). Results show that the gains from unsupervised discriminative training are highly dependent on the accuracy of the automatic transcriptions. © 2007 IEEE.

Towards measuring the impact of information availability on maintenance decisions

Decisions concerning maintenance have become increasingly important and requires a diverse set of information as systems become more complex. The availability of information has an impact on the effectiveness of these decisions, and thus on the performance of the asset. This paper highlights the importance of quantifying the value of information on maintenance decisions and asset performance. In particular, we emphasise the need to focus on measuring value as opposed to cost of maintenance, which is the current practice. In this direction, we propose a measure - Value of Ownership (VOO) - to assess the value of information and performance of maintenance decisions throughout an assets lifecycle. © 2009 IFAC.