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Total parenteral nutrition (TPN) administration in a mouse model leads to a local mucosal inflammatory response, resulting in a loss of epithelial barrier function (EBF). Although, the underlying mechanisms are unknown, a major contributing factor is a loss of growth factors and subsequent critical downstream signaling. An important component of these is the p-Akt pathway. An additional contributing factor to the loss of EBF with TPN is an increase in proinflammatory cytokine abundance within the mucosal epithelium, including TNF-α and IFN-γ. Loss of critical nutrients, including glutamine and glutamate, may affect EBF, contributing to the loss of tight junction proteins. Finding protective modalities for the small intestine during TPN administration may have important clinical applications. Supplemental glutamine and glutamate may be examples of such agents. [ABSTRACT FROM AUTHOR]

In speech recognition systems where the speaker and utterance environment cannot be designated, the drop in recognition precision due to the incompatibility of the input speech and acoustic model's training data is a problem. Although this problem is normally solved by speaker adaptation, sufficient precision cannot be achieved for speaker adaptation unless good-quality adaptation data can be obtained. In this paper, the authors propose a method of efficiently clustering large-scale data using the likelihoods of a cluster model that was created from small-scale data as the criteria to obtain a high-precision adapted acoustic model. They also propose a method of using the cluster model to automatically determine the adapted acoustic model during recognition from only the beginning of the sentences of the input speech. The results of applying the proposed technique to news speech recognition experiments show that the adapted acoustic model selection precision can be ensured by using only 0.5 second of data of the beginnings of sentences of the input speech and that the proposed technique achieves a reduction rate for invalid recognitions of 20% and a reduction in the time required for recognition of 23% compared with when the adapted acoustic model for each cluster is not used. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 88(2): 41–51, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20157 [ABSTRACT FROM AUTHOR]

A high-performance method for pitch extraction is proposed for the purposes of real-time sequential speech processing that can be used in such applications as speech rate conversion systems. According to this method, autocorrelation functions of the input speech waveforms are calculated for one analyzed point in time using multiple lengths of the analysis windows, and the largest peaks of each autocorrelation function are detected within the appropriate ranges, after which the optimum pitch period is selected by weighting the candidates of the pitch period obtained by the number of windows. Such selection processing is carried out independently for each analyzed point without using such characteristics as the continuity of the fundamental frequencies of the entire speech segment. This method was applied to analysis of a large number of speech materials, including recordings made by different speakers and speech samples mixed with noise. The tests have demonstrated that the proposed method features pitch extraction potentials superior to those of the cepstrum pitch determination method and the LPC residual autocorrelation method within a wide range of fundamental frequencies and power levels. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 83(2): 67–79, 2000 [ABSTRACT FROM AUTHOR]

This paper proposes a now method of high-quality pitch modification by complex cepstrum analysis-synthesis algorithm (hereafter referred to as the CCAS algorithm). The algorithm of the new conversion method presented here is as follows: the complex cepstra obtained within each pitch-synchronized frame are divided into pitch components and other spectral components by using comb filters. The pitch pulses obtained by inverse transformation of the pitch components are equalized adaptively to zero-phase, while the spectral complements also are transformed inversely into unit responses in time-domain. This equalization makes the pitch components more impulsive, resulting in clear separation of the voice source from the vocal tract characteristics. Speech synthesis is performed by convoluting the phase-adjusted pitch pulses with the nit responses. The results of listening tests showed that the pitch-modified speech by the CCAS method subjectively retains as high quality as the original speech. [ABSTRACT FROM AUTHOR]