Svm-hmm Landmark Based Speech Recognition

نویسندگان

  • Sarah Borys
  • Mark Hasegawa-Johnson
چکیده

Support vector machines (SVMs) are trained to detect acoustic-phonetic landmarks, and to identify both the manner and place of articulation of the phones producing each landmark with high accuracy. The discriminant outputs of these SVMs are used as input features for a standard HMM based ASR system. There is a significant improvement in both the phone and word recognition accuracy when using these SVM discriminant features when compared to the phone and word recognition accuracy of an MFCC based recognizer.

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تاریخ انتشار 2009