In this paper,we present a comparison of Khasi speech representations with four different spectral features and novel extension towards the development of Khasi speech corpora.These four features include linear predic...In this paper,we present a comparison of Khasi speech representations with four different spectral features and novel extension towards the development of Khasi speech corpora.These four features include linear predictive coding(LPC),linear prediction cepstrum coefficient(LPCC),perceptual linear prediction(PLP),and Mel frequency cepstral coefficient(MFCC).The 10-hour speech data were used for training and 3-hour data for testing.For each spectral feature,different hidden Markov model(HMM)based recognizers with variations in HMM states and different Gaussian mixture models(GMMs)were built.The performance was evaluated by using the word error rate(WER).The experimental results show that MFCC provides a better representation for Khasi speech compared with the other three spectral features.展开更多
为满足智能车辆的个性化需求,提高智能车辆人-机交互协同的满意度和接受度,构筑双层驾驶人跟驰模型框架,提出自适应驾驶人期望跟车间距和行为习惯的个性化驾驶人跟驰模型。首先,提取个体驾驶人跟驰均衡状态的数据,采用高斯混合和概率密...为满足智能车辆的个性化需求,提高智能车辆人-机交互协同的满意度和接受度,构筑双层驾驶人跟驰模型框架,提出自适应驾驶人期望跟车间距和行为习惯的个性化驾驶人跟驰模型。首先,提取个体驾驶人跟驰均衡状态的数据,采用高斯混合和概率密度函数(Gaussian Mixture Model and Probability Density Function,GMM-PDF)建立第1层模型,即驾驶人期望跟车距离模型。然后,将期望跟车距离参数引入模型,基于高斯混合-隐马尔可夫方法(Gaussian Mixture Model and Hidden Markov Model,GMM-HMM)学习驾驶习性,建立第2层模型预测加速度,即个性化驾驶人跟驰模型。其次,研究不同高斯分量个数对模型效果的影响,对比双层模型与Gipps模型、最优间距模型(Optimal Distance Model,ODM)、单层模型及通用模型的性能。最后,8位被试驾驶人的自然驾驶行为数据验证结果表明:高斯分量数量与模型性能存在一定的正相关性;在最优高斯分量数量下,8位被试驾驶人在训练集上预测误差均值为0.101 m·s^(-2),在测试集上为0.123 m·s^(-2);随机选取其中1位驾驶人的2个跟车片段数据进行模型计算,结果显示,加速度的平均误差绝对值分别为0.087 m·s^(-2)和0.096 m·s^(-2),预测效果优于Gipps模型、ODM模型、单层模型及通用模型30%以上,与驾驶人实际跟驰行为的吻合度更高。展开更多
基金supported by the Visvesvaraya Ph.D.Scheme for Electronics and IT students launched by the Ministry of Electronics and Information Technology(MeiTY),Government of India under Grant No.PhD-MLA/4(95)/2015-2016.
文摘In this paper,we present a comparison of Khasi speech representations with four different spectral features and novel extension towards the development of Khasi speech corpora.These four features include linear predictive coding(LPC),linear prediction cepstrum coefficient(LPCC),perceptual linear prediction(PLP),and Mel frequency cepstral coefficient(MFCC).The 10-hour speech data were used for training and 3-hour data for testing.For each spectral feature,different hidden Markov model(HMM)based recognizers with variations in HMM states and different Gaussian mixture models(GMMs)were built.The performance was evaluated by using the word error rate(WER).The experimental results show that MFCC provides a better representation for Khasi speech compared with the other three spectral features.
文摘为满足智能车辆的个性化需求,提高智能车辆人-机交互协同的满意度和接受度,构筑双层驾驶人跟驰模型框架,提出自适应驾驶人期望跟车间距和行为习惯的个性化驾驶人跟驰模型。首先,提取个体驾驶人跟驰均衡状态的数据,采用高斯混合和概率密度函数(Gaussian Mixture Model and Probability Density Function,GMM-PDF)建立第1层模型,即驾驶人期望跟车距离模型。然后,将期望跟车距离参数引入模型,基于高斯混合-隐马尔可夫方法(Gaussian Mixture Model and Hidden Markov Model,GMM-HMM)学习驾驶习性,建立第2层模型预测加速度,即个性化驾驶人跟驰模型。其次,研究不同高斯分量个数对模型效果的影响,对比双层模型与Gipps模型、最优间距模型(Optimal Distance Model,ODM)、单层模型及通用模型的性能。最后,8位被试驾驶人的自然驾驶行为数据验证结果表明:高斯分量数量与模型性能存在一定的正相关性;在最优高斯分量数量下,8位被试驾驶人在训练集上预测误差均值为0.101 m·s^(-2),在测试集上为0.123 m·s^(-2);随机选取其中1位驾驶人的2个跟车片段数据进行模型计算,结果显示,加速度的平均误差绝对值分别为0.087 m·s^(-2)和0.096 m·s^(-2),预测效果优于Gipps模型、ODM模型、单层模型及通用模型30%以上,与驾驶人实际跟驰行为的吻合度更高。
