【作者】 徐利强； 谢湘； 黄石磊； 李通；

【Author】 XU Li-qiang;XIE Xiang;HUANG Shi-lei;LI Tong;Institute of communication technology, Beijing Institute of Technology;Shenzhen Research Institute of Beijing Institute of Technology;

【机构】 北京理工大学通信技术研究所； 北京理工大学深圳研究院；

【摘要】 在我们的日常自然言语中,会出现哈欠、填音、掌声、笑声等非文字类的语音事件,它们在一定程度上会反映出聊天的气氛和背景,说话人的情感状态和性格等信息,所以对这些非文字类的语音事件的研究有助于语音副信息研究。本文主要关注与连续语音中的笑声检测问题,基于语音帧层面,使用ELM(极限学习机)算法的连续语音中的笑声检测的有效方法,并且基于笑声事件的连续特性以及时长特性对基于ELM算法的检测结果进行优化。更多还原

【Abstract】 In our daily natural speech, there are yawn, filling sound, applause, laughter and other non-text-based speech factors. To some extent, they reflect the atmosphere and background of the speech, the speaker’s emotional state, personality and other information. Therefore, researches on non-text speech are helpful to speech sub-information research. This thesis focuses on the laughter detection in continuous speech. Based on speech frame, ELM(Extreme Learning Machine) is used to detect laughter in continuous speech. Based on the continuous characteristics and duration characteristics in laughter, the test results in ELM algorithm are optimized.更多还原