An important component of a spoken term detection （STD） system involves estimating confidence measures of hypothesised detections.A potential problem of the widely used lattice-based confidence estimation,however,is...An important component of a spoken term detection （STD） system involves estimating confidence measures of hypothesised detections.A potential problem of the widely used lattice-based confidence estimation,however,is that the confidence scores are treated uniformly for all search terms,regardless of how much they may differ in terms of phonetic or linguistic properties.This problem is particularly evident for out-of-vocabulary （OOV） terms which tend to exhibit high intra-term diversity.To address the impact of term diversity on confidence measures,we propose in this work a term-dependent normalisation technique which compensates for term diversity in confidence estimation.We first derive an evaluation-metric-oriented normalisation that optimises the evaluation metric by compensating for the diverse occurrence rates among terms,and then propose a linear bias compensation and a discriminative compensation to deal with the bias problem that is inherent in lattice-based confidence measurement and from which the Term Specific Threshold （TST） approach suffers.We tested the proposed technique on speech data from the multi-party meeting domain with two state-ofthe-art STD systems based on phonemes and words respectively.The experimental results demonstrate that the confidence normalisation approach leads to a significant performance improvement in STD,particularly for OOV terms with phonemebased systems.展开更多
针对关键词中的集外词检索任务,提出采用音素、音节、词片三种子词单元进行多流信息的联合检索算法,其中对基于音素的语音检索(Spoken term detection,STD)系统使用基于n元语言模型-加权有限状态机的完全匹配检索降低漏警,对基于音节、...针对关键词中的集外词检索任务,提出采用音素、音节、词片三种子词单元进行多流信息的联合检索算法,其中对基于音素的语音检索(Spoken term detection,STD)系统使用基于n元语言模型-加权有限状态机的完全匹配检索降低漏警,对基于音节、词片的STD系统使用模糊匹配检索降低虚警,最后采用线性逻辑回归(Linear logistic regression,LLR)的算法将三个子系统的结果进行融合。在NIST STD 2006语音检索评测的英语电话会话语音测试集上的实验结果表明,相对于最好的单流系统,多流信息融合获得了12%的实际词项权重值(Actual term weighted value,ATWV)相对提升。展开更多
文摘An important component of a spoken term detection （STD） system involves estimating confidence measures of hypothesised detections.A potential problem of the widely used lattice-based confidence estimation,however,is that the confidence scores are treated uniformly for all search terms,regardless of how much they may differ in terms of phonetic or linguistic properties.This problem is particularly evident for out-of-vocabulary （OOV） terms which tend to exhibit high intra-term diversity.To address the impact of term diversity on confidence measures,we propose in this work a term-dependent normalisation technique which compensates for term diversity in confidence estimation.We first derive an evaluation-metric-oriented normalisation that optimises the evaluation metric by compensating for the diverse occurrence rates among terms,and then propose a linear bias compensation and a discriminative compensation to deal with the bias problem that is inherent in lattice-based confidence measurement and from which the Term Specific Threshold （TST） approach suffers.We tested the proposed technique on speech data from the multi-party meeting domain with two state-ofthe-art STD systems based on phonemes and words respectively.The experimental results demonstrate that the confidence normalisation approach leads to a significant performance improvement in STD,particularly for OOV terms with phonemebased systems.
文摘针对关键词中的集外词检索任务,提出采用音素、音节、词片三种子词单元进行多流信息的联合检索算法,其中对基于音素的语音检索(Spoken term detection,STD)系统使用基于n元语言模型-加权有限状态机的完全匹配检索降低漏警,对基于音节、词片的STD系统使用模糊匹配检索降低虚警,最后采用线性逻辑回归(Linear logistic regression,LLR)的算法将三个子系统的结果进行融合。在NIST STD 2006语音检索评测的英语电话会话语音测试集上的实验结果表明,相对于最好的单流系统,多流信息融合获得了12%的实际词项权重值(Actual term weighted value,ATWV)相对提升。
