TalkPrinting: New Features and Models for Automatic Speaker Recognition

自动说话人识别对于许多应用至关重要，从安全访问到情报收集，再到存档和理解对话。 目前的说话人识别系统模型的具体发言人的特点，但广泛的习惯和风格的差异才刚刚开始探索。 这些包括语调、能量和持续时间的模式，以及习惯性的单词和短语使用。 在这些异构的变化模式中利用信息在特征选择、建模和信息组合方面提出了挑战。 特征发现和选择工作将考虑可能可用的各种各样的风格特征。 工作的特征空间转换和建模阶段将使用降维和聚类来探索特征空间。 将对生成的特征进行建模，以关注特定类别的特征。 进一步的系统组合研究将研究如何将特定特征类型的单个系统最好地组合起来，以优化性能识别。 新的特征和建模方法将在年度说话人识别评估中进行评估。拟议的工作将导致识别表征个体说话人行为的新的可提取特征。 它探索更复杂的模型，以更好地捕捉复杂的行为和关系。 该项目通过提高识别性能，对情报、执法、安全和其他应用产生了影响。 由于新功能是基于表演行为而不是简单的声道生理，因此新功能也可用于情感识别或对话检测等任务。 这些系统将免费提供，并吸引代表性不足的研究生。

Automatic speaker recognition is critical for many applications, ranging from secure access to intelligence gathering, to archiving and understanding conversation. Current speaker recognition systems model specific speaker characteristics, but a vast range of habitual and stylistic differences has just begun to be explored. These include patterns of intonation, energy and duration, as well as habitual word and phrase usage. Exploiting information in these heterogeneous modes of variation presents challenges in feature selection, modeling, and information combination. Feature discovery and selection efforts will consider the large variety of stylistic features that may be available. The feature space transformation and modeling phase of the work will explore the feature space using dimensionality reduction and clustering. The resulting features will be modeled to focus on specific classes of features. Further system combination research will study how individual systems for specific feature types can best be combined to optimize performance recognition. The new features and modeling approaches will be evaluated in the annual Speaker Recognition Evaluation.The proposed work will lead to identification of new extractable features characterizing individual speaker behavior. It explores more sophisticated models to better capture complex behavior and relationships. The project has impact for intelligence, law enforcement, security and other application by enhancing recognition performance. Because the new features are based on performance behavior rather than simply vocal tract physiology, the new features can also be used for tasks such as emotion recognition or conversation detection. The systems will be freely available and engage under-represented graduate students.