SUMMARY: We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results. AVAILABILITY AND IMPLEMENTATION: StochSS Live! is freely available at https://live.stochss.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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The problem of labelling speaker turns by automatically segmenting and clustering a continuous audio stream is addressed. A new clustering scheme is presented and evaluated using a clustering efficiency score which treats both agglomerative and divisive clustering strategies equally. Results show an efficiency of 70% can be obtained on both manually and automatically derived segments on the 1996 Hub4 development data. For the task of identifying potentially unknown anchor speakers within broadcast news shows, the frame classification error rate is very important. To reflect this, a frame-based cluster efficiency is defined and the results show a 90% frame-based efficiency can be achieved. Finally a frame-based comparison between the manually and automatically derived segment/cluster sets shows that approximately one third of the errors are introduced during segmentation and two-thirds during clustering.

Our work deals with the classical problem of merging heterogenous and asynchronous parameters. It is well known that lip reading improves the speech recognition score, especially in noise conditions; so we study more precisely the modeling of the acoustic and labial parameters to propose two automatic speech recognition systems: a direct identification is performed by using a classical HMM approach, no correlation between visual and acoustic parameters is assumed; and two correlated models, a master HMM and a slave HMM, process respectively the labial observations and the acoustic ones. To assess each approach, we use a segmental pre-processing method. Our task is the recognition of spelled French letters, in clear and noisy (cocktail party) environments. Whatever the approach and conditions, the introduction of labial features improves the performance, but the difference between the two models is not enough sufficient to provide any priority.

This paper describes a new scheme for robust speech recognition systems where visual information and acoustic features are merged.Using as robust unit the « pseudo-diphone », we compare a global Hidden Markov Model (HMM) and a Master/Slave HMM through a centisecond preprocessing and through a segmental one.We confirm by experimentation the importance of articulatory features in clean and noisy environments.

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