Representations of Speech Dynamics as Features for Speaker Recognition

近年来，说话人识别领域取得了非常迅速的进展。自2005年以来，NIST说话人识别评估的错误率下降了2-3倍，2010年对“人类辅助说话人识别”的试点评估表明，NIST定义的核心说话人识别任务的机器性能与人类专家相当。 这一进展在很大程度上归功于特征信道建模和联合因子分析(JFA)等方法在减弱会话间可变性问题方面的成功。这些方法使得处理两种话语之间的差异是否可以通过假设不同的说话者或其他因素(如通道效应)来更好地解释这一根本问题成为可能。通过使用I向量(本质上是JFA模型中的隐藏变量)作为特征，该技术得到了极大的简化，这一发展使得设计非常轻量级、高性能的分类器成为可能。 值得注意的是，这一进展是使用高斯混合模型(GMM)作为语音生成的底层模型来实现的。GMM可以模拟说话人的声音音色，但它们无法提取有关语音动力学的任何信息，这些信息可能有助于区分说话人。使用各种类型的细分模型来解决这一问题的早期尝试无法与转基因生物相匹敌，这些方法只是在最近几年才零星地被重新考虑。我建议使用新的语音动力学生成模型来解决这个问题，这种模型的设计方式是，它们可以用来从语音数据中提取鲍姆-韦尔奇式的统计数据。这将允许与我已经开发的I-矢量/PLDA设备容易地集成。

There has been very rapid progress in the field of speaker recognition in recent years. Error rates in the NIST speaker recognition evaluations have decreased by a factor of 2--3 since 2005 and a pilot evaluation of "human assisted speaker recognition" in 2010 indicated that machine performance on the core speaker recognition task as defined by NIST is comparable to that of human experts. This progress is largely due to the success of methods such as eigenchannel modeling and Joint Factor Analysis (JFA) in attenuating the problem of intersession variability. These methods made it possible to get a handle on the fundamental question of whether the difference between two utterances could be better accounted for by postulating different speakers or other factors such as channel effects. The technology has been greatly simplified by using i-vectors (essentially the hidden variables in the JFA model) as features, a development which made it possible to design very light weight, high performance classifiers. It is remarkable that this progress has been achieved using Gaussian mixture models (GMMs) as the underlying generative model of speech. GMMs can model the timbre of a speaker's voice but they are incapable of extracting any information about speech dynamics which might be useful for distinguishing between speakers. Early attempts to tackle this problem using various types of segment models were no match for GMMs and these approaches have only been sporadically revisited in recent years. I propose to address this problem using new generative models of speech dynamics designed in such a way that they can be used to extract Baum-Welch-like statistics from speech data. This will allow for easy integration with the i-vector/PLDA apparatus that I have already developed.