To better demonstrate the economic forecasting function of business climate survey,China Economic Monitoring and Analysis Center(CEMAC) revised the calculation methodology of Business Climate Index(BCI) and Entreprene...To better demonstrate the economic forecasting function of business climate survey,China Economic Monitoring and Analysis Center(CEMAC) revised the calculation methodology of Business Climate Index(BCI) and Entrepreneur Confidence Index(ECI) from 2012Q 1.The details are as below:展开更多
To better demonstrate the economic forecasting function of business climate survey,China Economic Monitoring and Analysis Center(CEMAC)revised the calculation methodology of Business Climate Index(BCI)and Entrepreneur...To better demonstrate the economic forecasting function of business climate survey,China Economic Monitoring and Analysis Center(CEMAC)revised the calculation methodology of Business Climate Index(BCI)and Entrepreneur Confidence Index(ECI)from 2012Q1.The details are as below: Calculation methodology:Business Climate Index(BCI)is a weighted-average of current situation index (mainly reflects entrepreneurs’judgment on the business operation in the current situation)and expectation index (mainly reflects entrepreneurs’judgment on the business operation in the future),where current situation展开更多
Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy...Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language- independent （LI） with lightweight speaker-dependent （SD） automatic speech recognition （ASR） is a convenient option to solve tile problem. The dynamic time warping （DTW） algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several lhnitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping （MWDTW） algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model （HMM） significantly, and is also six times faster than DTW overall.展开更多
文摘To better demonstrate the economic forecasting function of business climate survey,China Economic Monitoring and Analysis Center(CEMAC) revised the calculation methodology of Business Climate Index(BCI) and Entrepreneur Confidence Index(ECI) from 2012Q 1.The details are as below:
文摘To better demonstrate the economic forecasting function of business climate survey,China Economic Monitoring and Analysis Center(CEMAC)revised the calculation methodology of Business Climate Index(BCI)and Entrepreneur Confidence Index(ECI)from 2012Q1.The details are as below: Calculation methodology:Business Climate Index(BCI)is a weighted-average of current situation index (mainly reflects entrepreneurs’judgment on the business operation in the current situation)and expectation index (mainly reflects entrepreneurs’judgment on the business operation in the future),where current situation
基金supported by the Research Plan Project of National University of Defense Technology under Grant No.JC13-06-01the OCRit Project made possible by the Global Leadership Round in Genomics&Life Sciences Grant(GL2)
文摘Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language- independent （LI） with lightweight speaker-dependent （SD） automatic speech recognition （ASR） is a convenient option to solve tile problem. The dynamic time warping （DTW） algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several lhnitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping （MWDTW） algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model （HMM） significantly, and is also six times faster than DTW overall.
