Sim-T: Simplify the Transformer Network by Multiplexing Technique for Speech Recognition

In recent years, a great deal of attention has been paid to the Transformer network for speech recognition tasks due to its excellent model performance. However, the Transformer network always involves heavy computation and large number of parameters, causing serious deployment problems in devices with limited computation sources or storage memory. In this paper, a new lightweight model called Sim-T has been proposed to expand the generality of the Transformer model. Under the help of the newly developed multiplexing technique, the Sim-T can efficiently compress the model with negligible sacrifice on its performance. To be more precise, the proposed technique includes two parts, that are, module weight multiplexing and attention score multiplexing. Moreover, a novel decoder structure has been proposed to facilitate the attention score multiplexing. Extensive experiments have been conducted to validate the effectiveness of Sim-T. In Aishell-1 dataset, when the proposed Sim-T is 48% parameter less than the baseline Transformer, 0.4% CER improvement can be obtained. Alternatively, 69% parameter reduction can be achieved if the Sim-T gives the same performance as the baseline Transformer. With regard to the HKUST and WSJ eval92 datasets, CER and WER will be improved by 0.3% and 0.2%, respectively, when parameters in Sim-T are 40% less than the baseline Transformer

The impact of spatial temporal averaging on the dynamic statistical properties of rain fields

Knowledge of the spatial-temporal variation of rain fields is required for the planning and optimization of wide area high frequency terrestrial and satellite communication networks. This paper presents data and a method for characterizing multi-resolutions statistical/dynamic parameters describing the spatial-temporal variation of rain fields across ocean climate in North- Western Europe. The data is derived from the NIMROD network of rain radars. The characterizing parameters include: (i) statistical distribution of point one-minute rainfall rates, (ii) spatial and temporal correlation function of rainfall rate and, (iii) the probability of rain/no-rain. The main contributions of this paper are the assessment of the impact of varying spatial and temporal integration lengths on these parameters, their dependencies on the integration volumes and area sizes, and the model for both temporal and spatial correlation parameters