图 书 馆
基于隐马尔可夫模型的分层Dirichlet过程Hierarchical-Dirichlet-Process-based Hidden Markov Models |
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| 课程网址: | http://videolectures.net/nipsworkshops09_sudderth_hdpbhmm/ |
| 主讲教师: | Erik Sudderth |
| 开课单位: | 布朗大学 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem is rendered particularly dicult by the fact that we are not allowed to assume knowledge of the number of people participating in the meeting. To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. (2006). Although the basic HDP-HMM tends to over-segment the audio data{creating redundant states and rapidly switching among them{we describe an augmented HDP-HMM that provides eff ective control over the switching rate. We also show that this augmentation makes it possible to treat emission distributions nonparametrically. To scale the resulting architecture to realistic diarization problems, we develop a sampling algorithm that employs a truncated approximation of the Dirichlet process to jointly resample the full state sequence, greatly improving mixing rates. Working with a benchmark NIST data set, we show that our Bayesian nonparametric architecture yields state-of-the-art speaker diarization results. |
| 关 键 词: | 贝叶斯非参数方法; 采样算法; 全状态序列 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-01：王勇彬(课程编辑志愿者) |
| 阅读次数: | 303 |