Confidence of agreement among multiple LVCSR models and model combination by SVM
Confidence of agreement among multiple LVCSR models and model combination by SVM
复制标题
多个 LVCSR 模型之间的一致性置信度以及 SVM 的模型组合
DOI：
10.1109/icassp.2003.1198705
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
S. Nakagawa
中科院分区：
文献类型：
--
作者：
T. Utsuro;Yasuhiro Kodama;Tomohiro Watanabe;H. Nishizaki;S. Nakagawa
对于语音识别系统的许多实际应用，对每个假想单词进行置信度估计是非常理想的。与以往关于置信度度量的工作不同，我们提出了从多个LVCSR模型的输出中提取置信度度量的特征。为了进一步分析所提出的置信度，本文考察了每个词的置信度与词的词性和音节长度等特征之间的相关性。然后，我们将支持向量机学习技术应用于组合多个LVCSR模型的输出的任务，其中，作为支持向量机学习的特征，输出假想单词的模型对等信息对于提高单词识别率是有用的。实验结果表明，与性能最好的单一模型相比，组合结果的相对词错减少高达72%，相对于Rover的相对词错减少高达36%。
For many practical applications of speech recognition systems, it is quite desirable to have an estimate of confidence for each hypothesized word. Unlike previous works on confidence measures, we have proposed features for confidence measures that are extracted from outputs of more than one LVCSR models. For further analysis of the proposed confidence measure, this paper examines the correlation between each word's confidence and the word's features such as its part-of-speech and syllable length. We then apply SVM learning technique to the task of combining outputs of multiple LVCSR models, where, as features of SVM learning, information such as the pairs of the models which output the hypothesized word are useful for improving the word recognition rate. Experimental results show that the combination results achieve a relative word error reduction of up to 72 % against the best performing single model and that of up to 36 % against ROVER.
T.Kawahara,T.Kobayashi,K.Takeda,N.Minematsu,K.Itou,M.Yamamoto,A.Yamada,T.Utsuro,K.Shikano：“用于日语大词汇连续语音识别的可共享软件存储库”Proc。
DOI：
--
发表时间：
--
期刊：
影响因子：
--
作者：
通讯作者：
--