Comparative feature on design, construction and installation of three different model of low cost solar tunnel dryers

The study was conducted in collaboration with the ECFC project of the FAO (BGD/97/017) in Cox's Bazar to develop a low cost solar tunnel dryer for the production of high quality marine dried fish. The study areas were Kutubdiapara, Maheshkhali and Shahparirdip under Cox's Bazar district. Three different models of low cost solar dryer were constructed with locally available materials such as bamboo, wood, bamboo mat, hemp, canvas, wire, nails, rope, tin, polythene and net. Size of the dryers were: 20x4x3 ft ; 30x3x3 ft and 65x3x3 ft with the costs of Tk. 3060, 3530, 9600 for dryer 1, 2 and 3, respectively having different models. The drying capacities were 50, 150, 500 kg for dryer 1, 2 and 3 respectively. The average temperature range inside the dryers were 29-43°C, 34-51°C and 37-57°C for dryer 1, 2 and 3 respectively as recorded at 8:30h to 16:30h. The relative humidity were in the ranges of 22-42%, 27-39% and 24-41 % in dryer 1, 2 and 3 respectively. The fish samples used were Bombay duck, Silver Jew fish and Ribbon fish. The total drying time was in the range of 30-42, 28-38 and 24-34 hours to reach the moisture content of 12.3-14.5, 11.8-14.3, and 11.6-14.1% in dryer 1, 2 and 3 respectively. Among these three fish samples the drying was faster in Silver Jew fish followed by Bombay duck and Ribbon fish in all the three dryer.

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.