提出了用于音素識別的K子空間和時延自相關(guān)器神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)，用將時延設(shè)計加入線性自相關(guān)器，以擴展音素濾波神經(jīng)網(wǎng)絡(luò)的方法，產(chǎn)生p維子空間，并采用迭代過程修改劃分，以便捕獲語音信號中的時間序列信息。這種帶不分類訓(xùn)練過程的體系結(jié)構(gòu)提供了一種高識別性能的方法，沒有大多數(shù)常規(guī)語音識別神經(jīng)網(wǎng)絡(luò)所常有的網(wǎng)絡(luò)輸出值不表示候選者似然性的缺陷。通過英語音素和漢語音素的初步試驗，識別正確率為84.38%，比音素濾波神經(jīng)網(wǎng)絡(luò)方法好。

A neural network architecture, K-subspaces and time-delay auto-associators, is proposed for phoneme recognition. It extends the phoneme filter neural networks approach by adding linear auto-associators to create p-dimension subspace, and an iteration is employed to improve the decision. It is good to capture the time-sequence information in speech signal. The architecture proposed could provide a high recognition performance without traditional neural network’s shortcoming. Some recognition simulations for both English and Chinese phonemes are conducted, and the recognition rate is 84.38% which is better than phoneme filter neural networks approach.