Structure-Optimized Speech Recognition with Deterministic Annealing

统计语音识别的研究将沿着两条中心线进行。一个研究方向是关于在给定有限训练数据的情况下确定识别器的最佳结构和复杂性的方法的发展，其中最优性是关于训练集之外的预期性能。底层框架是一个结构复杂性参数空间，其中点对应于识别系统的类型，最优轨迹表示明智地增加系统复杂性以提高训练集上的性能。将开发许多用于执行这种权衡的正交方法。轨迹上的最优点（系统）将通过开发适当的交叉验证技术来确定，该技术可以估计训练集之外的性能。互补的研究方向涉及系统设计中使用的优化技术，这决定了系统的最终性能。确定性退火方法将得到扩展和发展，成为语音识别的强大优化工具，特别是用于上述结构参数空间内的优化操作，从而直接最小化训练集外的预期误分类率。该项目预计将产生强大的语音识别器设计技术，从而有可能将计算机的可访问性扩展到公众，并提高许多应用的可行性，从声控设备到自动电话服务。

Research in statistical speech recognition will be conducted along two central lines. One research direction is concerned with the development of approaches to determine the optimal structure and complexity of the recognizer given limited training data, where optimality is with respect to expected performance outside the training set. The underlying framework is a structural complexity parameter space where points correspond to types of recognition systems, and optimal trajectories represent judicious trading of increase in system complexity for improved performance on the training set. A number of orthogonal means for performing such tradeoffs will be developed. The optimal point (system) on the trajectory will be determined by development of appropriate cross-validation techniques that estimate performance outside the training set. The complementary research direction involves the optimization technique used in the system design, which determines its ultimate performance. The deterministic annealing approach will be extended and developed as a powerful optimization tool for speech recognition and, in particular, for optimization operations within the above structural parameter space so as to directly minimize the expected rate of misclassification outside the training set. The project is expected to result in powerful speech recognizer design techniques and thereby potentially extend the accessibility of computers to the public, and enhance the feasibility of numerous applications, ranging from voice-activated devices to automated telephony services.