Acoustic and language modeling of human and nonhuman noises for human-to-human spontaneous speech recognition
Acoustic and language modeling of human and nonhuman noises for human-to-human spontaneous speech recognition
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用于人与人自发语音识别的人类和非人类噪声的声学和语言建模
DOI：
10.1109/icassp.1995.479531
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
I. Rogina
中科院分区：
文献类型：
--
作者：
Tanja Schultz;I. Rogina
我们的语音到语音翻译系统JANUS自发人与人对话的几个改进。自发语音中的常见现象进行了描述，其次是不同类型的噪声的分类。为了处理人与人对话中的各种自发效果，引入了代表人类和非人类噪声以及单词片段的特殊噪声模型。结果表明，声学和语言建模的噪声显着提高识别性能。在实验中，进行噪声类的聚类，并比较得到的聚类变体，从而允许确定模型的灵敏度和可训练性之间的最佳权衡。
Several improvements of our speech-to-speech translation system JANUS on spontaneous human-to-human dialogs are presented. Common phenomena in spontaneous speech are described, followed by a classification of different types of noise. To handle the variety of spontaneous effects in human-to-human dialogs, special noise models are introduced representing both human and nonhuman noise, as well as word fragments. It is shown that both the acoustic and the language modeling of the noise increase the recognition performance significantly. In the experiments, a clustering of the noise classes is performed and the resulting cluster variants are compared, thus allowing one to determine the best tradeoff between the sensitivity and trainability of the models.