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Abstract:To • In brain death cases who showed nonfilling phenomena in the internal carotid angiograms, the blood flow velocity patterns of the common carotid arteries were characterized by involvement of a single systolic peak and a marked reverse flow component which had never been observed in healthy subjects.The individuality of each blood flow velocity pattern in the common, internal and external carotid arteries was made clear by placing the transducer in contact with the respective artery in a certain case. The Doppler signal from the internal carotid artery involving a signal from a reverse flow was slightly detectable, even if the blood pressure was elevated by norepinephrine infusion and the external carotid artery was temporarily compressed. The blood flow velocity pattern of the external carotid artery was similar to the pattern of the common carotid artery. The peculiar flow pattern indicates that a brain death case has a to-and-fro movement in the internal carotid blood flow and an external carotid escape of common carotid arterial blood.

SUMMARYTo achieve high quality output speech synthesis systems, data-driven grapheme-to-phoneme (G2P) conversion is usually used to generate the phonetic transcription of out-of-vocabulary (OOV) words. To improve the performance of G2P conversion, this paper deals with the problem of conflicting phonemes, where an input grapheme can, in the same context, produce many possible output phonemes at the same time. To this end, we propose a two-stage neural network-based approach that converts the input text to phoneme sequences in the first stage and then predicts each output phoneme in the second stage using the phonemic information obtained. The first-stage neural network is fundamentally implemented as a many-to-many mapping model for automatic conversion of word to phoneme sequences, while the second stage uses a combination of the obtained phoneme sequences to predict the output phoneme corresponding to each input grapheme in a given word. We evaluate the performance of this approach using the American English words-based pronunciation dictionary known as the auto-aligned CMUDict corpus [1]. In terms of phoneme and word accuracy of the OOV words, on comparison with several proposed baseline approaches, the evaluation results show that our proposed approach improves on the previous one-stage neural network-based approach for G2P conversion. The results of comparison with another existing approach indicate that it provides higher phoneme accuracy but lower word accuracy on a general dataset, and slightly higher phoneme and word accuracy on a selection of words consisting of more than one phoneme conflicts. key words: two-stage neural network, grapheme-to-phoneme conversion, many-to-many mapping, prediction through phonemic information, phoneme conflict

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The precise conversion of arbitrary text into its corresponding phoneme sequence (grapheme-to-phoneme or G2P conversion) is implemented in speech synthesis and recognition, pronunciation learning software, spoken term detection and spoken document retrieval systems. Because the quality of this module plays an important role in the performance of such systems and many problems regarding G2P conversion have been reported, we propose a novel two-stage model-based approach, which is implemented using an existing weighted finite-state transducer-based G2P conversion framework, to improve the performance of the G2P conversion model. The first-stage model is built for automatic conversion of words to phonemes, while the second-stage model utilizes the input graphemes and output phonemes obtained from the first stage to determine the best final output phoneme sequence. Additionally, we designed new grapheme generation rules, which enable extra detail for the vowel and consonant graphemes appearing within a word. When compared with previous approaches, the evaluation results indicate that our approach using rules focusing on the vowel graphemes slightly improved the accuracy of the out-of-vocabulary dataset and consistently increased the accuracy of the in-vocabulary dataset.