Kelly Finnian, Hansen John H L
Center for Robust Speech Systems (CRSS), University of Texas at Dallas, Richardson, TX 75083-0688 USA.
IEEE/ACM Trans Audio Speech Lang Process. 2021;29:927-942. doi: 10.1109/taslp.2021.3053388. Epub 2021 Jan 21.
Variations in vocal effort can create challenges for speaker recognition systems that are optimized for use with neutral speech. The Lombard effect and whisper are two commonly-occurring forms of vocal effort variation that result in non-neutral speech, the first due to noise exposure and the second due to intentional adjustment on the part of the speaker. In this article, a comparative evaluation of speaker recognition performance in non-neutral conditions is presented using multiple Lombard effect and whisper corpora. The detrimental impact of these vocal effort variations on discrimination and calibration performance on global, per-corpus, and per-speaker levels is explored using conventional error metrics, along with visual representations of the model and score spaces. A non-neutral speech detector is subsequently introduced and used to inform score calibration in several ways. Two calibration approaches are proposed and shown to reduce error to the same level as an optimal calibration approach that relies on ground-truth vocal effort information. This article contributes a generalizable methodology towards detecting vocal effort variation and using this knowledge to inform and advance speaker recognition system behavior.
发声力度的变化会给针对中性语音优化的说话人识别系统带来挑战。伦巴德效应和低语是两种常见的发声力度变化形式，会导致非中性语音，第一种是由于噪声暴露，第二种是由于说话者的有意调整。在本文中，使用多个伦巴德效应和低语语料库对非中性条件下的说话人识别性能进行了比较评估。使用传统误差指标以及模型和分数空间的可视化表示，探讨了这些发声力度变化对全局、每个语料库和每个说话人层面的辨别和校准性能的不利影响。随后引入了一个非中性语音检测器，并以多种方式用于指导分数校准。提出了两种校准方法，并证明它们可以将误差降低到与依赖真实发声力度信息的最优校准方法相同的水平。本文贡献了一种可推广的方法，用于检测发声力度变化，并利用这些知识来指导和改进说话人识别系统的行为。
