JFA for text-dependent speaker verification

VoiceTrust需要能够使用短持续时间（1-2秒）的话语对说话者进行身份验证。它们的应用需要一种对信道效应具有鲁棒性的技术（例如，发言者可以在注册和验证时使用不同的手机）。 此外，这种鲁棒性需要使用适量的数据进行分类器训练来实现。为了满足持续时间的限制，说话人验证需要是“文本相关的”（也就是说，基于密码短语）。 这排除了使用说话者验证的文本无关方法的现有技术方法，特别是i向量/PLDA方法。 我们建议通过开发一个版本的联合因子分析（JFA，i-向量/PLDA的前身），这是专为文本相关的说话人识别问题，它可以用适量的数据进行训练，以满足VoiceTrust的要求。 我们将开发两种解决方案来解决这个问题，一种设计用于处理有用于建模密码短语的背景数据的情况，另一种设计用于处理一般情况。(The在一般情况下，技术挑战更大，但这里的成功将减轻VoiceTrust收集数据的负担。）为了实现对加性背景噪声和混响的鲁棒性，我们将开发几个声学特征提取器，并在每种情况下训练JFA后端（因此每个前端都有一个分类器）。这些分类器产生的分数将通过逻辑回归进行融合，以做出说话人确认决策。 最后，VoiceTrust特别关注说话人识别技术容易受到语音合成和语音转换技术的欺骗攻击。我们将创建合成数据集，并使用它们来训练类似JFA的合成和语音转换工件的统计模型，以检测欺骗攻击。 这项技术将被集成到现有的VoiceTrust产品，VT-Athena和VT-Aura中。

VoiceTrust needs to be able to authenticate speakers using utterances of short duration (1-2 seconds). Their applications require a technology which is robust to channel effects (so that, for example, speakers can use different handsets at enrollment and verification time). Furthermore this robustness needs to be achieved using modest amounts of data for classifier training. To satisfy the duration constraint, speaker verification needs to be ``text-dependent'' (that is, based on passphrases). This precludes the use of state of the art methods of text-independent methods of speaker verification and, in particular, the i-vector/PLDA approach. We propose to satisfy VoiceTrust's requirements by developing a version of Joint Factor Analysis (JFA, the predecessor of i-vector/PLDA) which is tailored to the text-dependent speaker recognition problem and which can be trained with modest amounts of data. We will develop two solutions to the problem, one designed to handle the case where background data for modeling passphrases is available and the other to handle the general case. (The technical challenge is greater in the general case but success here would relieve VoiceTrust of the burden of collecting data.) To achieve robustness to additive background noise and reverberation we will develop several acoustic feature extractors and train a JFA back end in each case (so that there will be one classifier for each front end). Scores produced by these classifiers will be fused by logistic regression to make speaker verification decisions. Finally VoiceTrust is particularly concerned that speaker recognition technology is vulnerable to spoofing attacks by speech synthesis and voice conversion technology. We will create synthetic datasets and use them to train JFA-like statistical models of synthesis and voice conversion artifacts in order to detect spoofing attacks. This technology will be integrated into currently existing VoiceTrust products, VT-Athena and VT-Aura.