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Reactive free radical species (R.) are associated with several forms of tissue damage and disease, and also with the process of aging. Protection is thought to be available in the form of endogenous compounds that react with and thereby "scavenge" the R.. Because many R. are reactive forms of oxygen, an effective scavenger is often referred to as an antioxidant. To be an effective antioxidant physiologically, a substance must have certain chemical and biological properties: it must be present in adequate amounts in the body; it must react with a variety of R.; it must be suitable for compartmentation; it must be readily available; it might be suitable for regeneration; it must be conserved by the kidneys; and it must have tolerable toxicity. Several water-soluble candidates are mentioned, with most having no more than one or two of the attributes listed. Ascorbic acid is discussed in detail, and an analysis is made of whether it has the properties mentioned.

Major progress is being recorded regularly on both the technology and exploitation of automatic speech recognition (ASR) and spoken language systems. However, there are still technological barriers to flexible solutions and user satisfaction under some circumstances. This is related to several factors, such as the sensitivity to the environment (background noise), or the weak representation of grammatical and semantic knowledge.Current research is also emphasizing deficiencies in dealing with variation naturally present in speech. For instance, the lack of robustness to foreign accents precludes the use by specific populations. Also, some applications, like directory assistance, particularly stress the core recognition technology due to the very high active vocabulary (application perplexity). There are actually many factors affecting the speech realization: regional, sociolinguistic, or related to the environment or the speaker herself. These create a wide range of variations that may not be modeled correctly (speaker, gender, speaking rate, vocal effort, regional accent, speaking style, non-stationarity, etc.), especially when resources for system training are scarce. This paper outlines current advances related to these topics.

We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appears to give better results than a conventional model, and the extra structure offers many new opportunities for modeling innovations while maintaining compatibility with most standard techniques.

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