A log-index weighted cepstral distance measure is proposed and tested in speaker-independent and speaker-dependent isolated word recognition systems using statistic techniques. The weights for the cepstral coefficient...A log-index weighted cepstral distance measure is proposed and tested in speaker-independent and speaker-dependent isolated word recognition systems using statistic techniques. The weights for the cepstral coefficients of this measure equal the logarithm of the corresponding indices. The experimental results show that this kind of measure works better than any other weighted Euclidean cepstral distance measures on three speech databases. The error rate obtained using this measure is about 1.8 percent for three databases on average, which is a 25% reduction from that obtained using other measures, and a 40% reduction from that obtained using Log Likelihood Ratio (LLR) measure. The experimental results also show that this kind of distance measure works well in both speaker-dependent and speaker-independent speech recognition systems.展开更多
水泥行业是工业碳排放的主要来源之一,我国是目前世界水泥产量最大的国家,探究水泥碳减排对实现碳达峰、碳中和目标至关重要。文章以安徽省为例,采用联合国政府间气候变化专门委员会(Intergovernmental Panel on Climate Change,IPCC)...水泥行业是工业碳排放的主要来源之一,我国是目前世界水泥产量最大的国家,探究水泥碳减排对实现碳达峰、碳中和目标至关重要。文章以安徽省为例,采用联合国政府间气候变化专门委员会(Intergovernmental Panel on Climate Change,IPCC)碳排放系数法计算安徽省水泥行业2010-2021年的碳排放量,并通过构建对数平均迪氏指数法(log-mean Divisia index,LMDI)因素分解模型分析安徽省水泥行业碳排放的主要影响因素。同时,对随机性的环境影响评估(stochastic impacts by regression on population,affluence and technology,STIRPAT)模型进行扩展,设定基准、低碳和强化低碳3种情景,建立安徽省水泥行业碳排放预测模型,预测不同情景下碳排放量和趋势。结果表明:安徽省水泥行业碳排放量总体呈上升趋势,且煤炭消耗量占比较大,节能减排面临较大压力;能源强度和劳动生产率是对安徽省碳排放影响最显著的因子;在基准、低碳和强化低碳情景下,安徽省水泥行业的碳排放峰值年分别为2030年之后、2025年和2023年。展开更多
文摘A log-index weighted cepstral distance measure is proposed and tested in speaker-independent and speaker-dependent isolated word recognition systems using statistic techniques. The weights for the cepstral coefficients of this measure equal the logarithm of the corresponding indices. The experimental results show that this kind of measure works better than any other weighted Euclidean cepstral distance measures on three speech databases. The error rate obtained using this measure is about 1.8 percent for three databases on average, which is a 25% reduction from that obtained using other measures, and a 40% reduction from that obtained using Log Likelihood Ratio (LLR) measure. The experimental results also show that this kind of distance measure works well in both speaker-dependent and speaker-independent speech recognition systems.
文摘水泥行业是工业碳排放的主要来源之一,我国是目前世界水泥产量最大的国家,探究水泥碳减排对实现碳达峰、碳中和目标至关重要。文章以安徽省为例,采用联合国政府间气候变化专门委员会(Intergovernmental Panel on Climate Change,IPCC)碳排放系数法计算安徽省水泥行业2010-2021年的碳排放量,并通过构建对数平均迪氏指数法(log-mean Divisia index,LMDI)因素分解模型分析安徽省水泥行业碳排放的主要影响因素。同时,对随机性的环境影响评估(stochastic impacts by regression on population,affluence and technology,STIRPAT)模型进行扩展,设定基准、低碳和强化低碳3种情景,建立安徽省水泥行业碳排放预测模型,预测不同情景下碳排放量和趋势。结果表明:安徽省水泥行业碳排放量总体呈上升趋势,且煤炭消耗量占比较大,节能减排面临较大压力;能源强度和劳动生产率是对安徽省碳排放影响最显著的因子;在基准、低碳和强化低碳情景下,安徽省水泥行业的碳排放峰值年分别为2030年之后、2025年和2023年。
