Binary-Pair Partitioned Neural Networks for Speaker and Dialect Recognition

小行星9217436 这是一个为期两年的连续奖励的第二年资助。 本计画探讨使用二进制对分割（Binary-pair partitioning，BPP）方法，以N*（N-1）/2对判别分类器分割N路分类任务，应用于说话人与方言辨识问题。 BPP网络的复杂性随着N2而增长，并且已经以用于说话者识别的初步方式检查的方法非常适合于说话者识别任务涉及大量说话者（N100）的情况。 使用BPP方法进行说话人识别的初步研究涉及DARPA/TIMIT数据库中三个方言区的100名男性说话人。 该系统在文本中的准确度为100%，独立于文献中报道的类似任务的结果。 BPP方法也适用于可以组合多个基本决策以获得总体决策的任务，例如方言或语言识别。 对该方法的改进和扩展将被研究，例如（1）输入特征的增强，（2）噪声和信道失真对性能的影响，（3）说话人确认模式以及识别模式中的性能，（4）性别和方言识别的性能，（5）该方法可能提供的语音识别的改进，以及（6）训练BPP网络以最小化错误概率而不是均方误差。

9217436 Zahorian This is the second year funding of a two year continuing award. This project investigates the use of the binary-pair partitioning (BPP) method, which partitions an N-way classification task with N*(N-1)/2 pair-discrimination classifiers, in the problem of speaker and dialect identification. The complexity of the BPP networks grows with N2 and the method, which has been examined in a preliminary way for speaker identification, is well suited for cases where the talker identification task involves a large number of speakers (N100). The preliminary studies of speaker identification with the BPP method involved 100 male speakers belonging to three dialect regions of the DARPA/TIMIT database. The system performed with 100% accuracy in a text independent than results reported in the literature for similar tasks. The BPP approach is also suited for tasks where multiple elemental decisions can be combined to obtain an overall decision, such as dialect or language identification. Refinements and extensions to the method will be investigated, such as (1) enhancement of the input features, (2) the effects of noise and channel distortion on performance, (3) performance in a speaker verification mode as well as in an identification mode, (4) performance in sex and dialect identification, (5) improvements that this method may provide for speech recognition, and (6) training of BPP networks to minimize the probability of error rather than the mean square error.