The Role of Dynamic Features in Text-Dependent and -Independent Speaker Verification
The Role of Dynamic Features in Text-Dependent and -Independent Speaker Verification
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动态特征在文本相关和独立说话人验证中的作用
DOI：
10.1109/icassp.2006.1660109
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
M. Carey
中科院分区：
文献类型：
--
作者：
Y. Liu;M. Russell;M. Carey
分段隐马尔可夫模型（SHMM）是一种隐马尔可夫模型（HMM），其状态与声学特征向量（或分段）的序列相关联，而不是单个向量。例如，通过将片段视为同质单元，可以开发更好的语音动态模型。本文考虑了一种基于概率的分段HMM用于说话人识别的潜在好处。本文给出了在YOHO上进行的与文本相关的说话人确认（TD-SV）和在交换机上进行的与文本无关的说话人确认（TI-SV）的结果。YOHO结果显示，与传统的HMM相比，使用分段模型的错误接受率降低了44%，而Switchboard结果相对于传统的高斯混合模型（GMM）系统没有任何改善。进行了进一步的实验来解释这些结果。他们指出，“分段GMM”的优先事项是模拟静止区域，并阐明δ参数在常规TI-SV中的作用
A segmental hidden Markov model (SHMM) is a hidden Markov model (HMM) whose states are associated with sequences of acoustic feature vectors (or segments), rather than individual vectors. By treating segments as homogeneous units it is possible, for example, to develop better models of speech dynamics. This paper considers the potential benefits of a trajectory-based segmental HMM for speaker recognition. Text-dependent speaker verification (TD-SV) results obtained on YOHO and text-independent speaker verification (TI-SV) results on switchboard are presented. The YOHO results show a 44% reduction in false acceptances using the segmental model compared with a conventional HMM, while the Switchboard results do not show any improvement relative to a conventional Gausian mixture model (GMM) system. Further experiments were conducted to explain these results. They indicate that the priority of a "segmental GMM" is to model stationary regions and shed light on the role of delta parameters in conventional TI-SV