Succession of bacterial genera during iced storage of three species of tropical prawns, Penaeus indicus, Matapenaeus dobsoni and M. affinis

The native bacterial flora of ocean fresh tropical prawns, Penaeus indicus, Metapenaeus dobsoni and M. affinis was more or less similar, mainly consisting of Pseudomonas, Acinetobacter, Moraxella and Arthrobacter. A definite succession of bacterial genera during iced storage was observed in these prawns. As the day of ice storage increased, the proportion of Acinetobacter and Moraxella also increased considerably and constituted 70-78% of the flora at the time of spoilage. Spoilage by Pseudomonas was very not significant in prawns under iced storage.

Unsupervised intra-lingual and cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction

Hidden Markov model (HMM)-based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to estimate the transcription of the adaptation data. This paper first presents an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for such supplementary acoustic models. This is achieved by defining a mapping between HMM-based synthesis models and ASR-style models, via a two-pass decision tree construction process. Second, it is shown that this mapping also enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data. Third, this paper demonstrates how this technique lends itself to the task of unsupervised cross-lingual adaptation of HMM-based speech synthesis models, and explains the advantages of such an approach. Finally, listener evaluations reveal that the proposed unsupervised adaptation methods deliver performance approaching that of supervised adaptation.