To further explore the relevance between prosody and syntax information, we propose a novel approach of prosodic modeling with rich syntactic context instead of prosodic structure in HMM-based Mandarin speech synthesis. Considering the characteristics of Mandarin itself, word-based and characterbased syntactic parsings are investigated in this study respectively. This method can not only avoid the existing cascade error in conventional way of prosodic parameter prediction but also not rely on the manually annotated corpora of prosodic structure. Experimental results show that even though automatic syntactic parsing has limited precision, prosodic modeling with rich syntactic context could still achieve significant better performance than the way of the manually annotated prosodic corpora, especially in duration evaluation.
To explore the potential of prosody for Mandarin speech recognition, this paper addresses the tone modeling problem and its integration issue. This study adopts the maximum entropy approach to capture both acoustic and lexical characteristics of tones due to its flexibility in handling multiple interacting features. Moreover, considering the phoneme factor, besides a tone model, a phoneme dependent model is also constructed. With regard to the model integration, the presented models are integrated into the recognizer under the one-pass decoding framework, where they are used to prune the active wordfinal states during beam search. Experimental results on the HUB-4 evaluation material reveal the effectiveness of the presented models. They significantly improve the performance of speech recognition with 7.6% and 11.1% relative reduction of character error rate.
Cardiopulmonary diseases, including cardiovascular disease (CVD) and chronic obstructive pulmonary disorder (COPD), are prevalent in the elderly population. Early identification, long-term health monitoring, and health management of cardiopulmonary disease are crucial for reversing organ damage and preventing further injury. However, most home health monitoring (HHM) devices need to be paired with a specific smartphone application, which leads to the complexity of monitoring multiple health indicators and hinders the feasibility of comprehensive multi-indicator analysis. Therefore, this paper designed a human cardiopulmonary health monitoring system based on an intelligent gateway to reduce the dependence of HHM on smartphones, achieve synchronous monitoring of multiple health indicators, and improve usability to better serve the elderly population. The proposed system can simultaneously monitor electrocardiogram (ECG), pulmonary function, blood pressure, and blood oxygen level (SpO2); process and analyze the data in real-time through the intelligent gateway’s edge computing power; and display the cardiopulmonary health status in real-time. The intelligent gateway embedded a specially designed CNN-LSTM artificial intelligence model on the STM32F429 microcontroller to realize real-time identification of ECG signals at the edge. The accuracy of the pretrained CNN-LSTM model for ECG signal identification is 99.49%, and the model has good performance in terms of complexity and RAM space occupied. According to the evaluation test, the system can achieve the purpose of monitoring human cardiopulmonary health, has a wide range of application scenarios, and has great value in promotion and application.
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