A pilot investigation into the effects of acute normobaric hypoxia, high altitude exposure and exercise on serum angiotensin-converting enzyme, aldosterone and cortisol

Introduction. Aldosterone decreases at high altitude (HA) but the effect of hypoxia on angiotensin converting-enzyme (ACE), a key step in the renin-angiotensin-aldosterone system, is unclear. Materials and Methods. We investigated the effects of exercise and acute normobaric hypoxia (NH, ~11.0% FiO2) on nine participants and six controls undertaking the same exercise at sea-level (SL). NH exposure lasted 5 hours with 90 min of submaximal treadmill walking. Blood samples for aldosterone, ACE and cortisol were taken throughout exposure and at rest during a trek to HA (5140 m) in eight separate participants. Results. There was no difference in cortisol or aldosterone between groups pre-exercise. Aldosterone rose with exercise to a greater extent at SL than in NH (post-exercise: 700±325 vs 335±238 pmol/L, mean ± SD, p=0.044). Conversely, cortisol rose to a greater extent in NH (post-exercise: 734±165 vs 344±159 nmol/L, mean ± SD, p=0.001). There were no differences in ACE activity. During the trek to HA resting aldosterone and cortisol reduced with no change in ACE. Conclusion. Acute NH subdues the exercise-associated rise in aldosterone but stimulates cortisol, whereas prolonged exposure at HA reduces both resting aldosterone and cortisol. As ACE activity was unchanged in both environments this is not the mechanism underlying the fall in aldosterone

Automatic recognition of schwa variants in spontaneous Hungarian speech

This paper analyzes the nature of the process involved in optional vowel reduction in Hungarian, and the acoustic structure of schwa variants in spontaneous speech. The study focuses on the acoustic patterns of both the basic realizations of Hungarian vowels and their realizations as neutral vowels (schwas), as well as on the design, implementation, and evaluation of a set of algorithms for the recognition of both types of realizations from the speech waveform. The authors address the question whether schwas form a unified group of vowels or they show some dependence on the originally intended articulation of the vowel they stand for. The acoustic study uses a database consisting of over 4,000 utterances extracted from continuous speech, and recorded from 19 speakers. The authors propose methods for the recognition of neutral vowels depending on the various vowels they replace in spontaneous speech. Mel-Frequency Cepstral Coefficients are calculated and used for the training of Hidden Markov Models. The recognition system was trained on 2,500 utterances and then tested on 1,500 utterances. The results show that a neutral vowel can be detected in 72% of all occurrences. Stressed and unstressed syllables can be distinguished in 92% of all cases. Neutralized vowels do not form a unified group of phoneme realizations. The pronunciation of schwa heavily depends on the original articulation configuration of the intended vowel

Modeling Prosodic Structures in Linguistically Enriched Environments

Abstract. A significant challenge in Text-to-Speech (TtS) synthesis is the formulation of the prosodic structures (phrase breaks, pitch accents, phrase accents and boundary tones) of utterances. The prediction of these elements robustly relies on the accuracy and the quality of error-prone linguistic procedures, such as the identification of the part-of-speech and the syntactic tree. Additional linguistic factors, such as rhetorical relations, improve the naturalness of the prosody, but are hard to extract from plain texts. In this work, we are proposing a method to generate enhanced prosodic events for TtS by utilizing accurate, error-free and high-level linguistic information. We are also presenting an appropriate XML annotation scheme to encode syntax, grammar, new or given information, phrase subject/object information, as well as rhetorical elements. These linguistically enriched has have been utilized to build realistic machine learning models for the prediction of the prosodic structures in terms of segmental information and ToBI marks. The methodology has been applied by exploiting a Natural Language Generator (NLG) system. The trained models have been built using classification via regression trees and the results strongly indicate the realistic effect on the generated prosody. The evaluation of this approach has been made by comparing the models produced by the enriched documents to those produced by plain text of the same domain. The results show an improved accuracy of up to 23%. 1