On HMM Speech Recognition Based on Complex Speech Analysis
Tatsuhiko Kinjo, Keiichi Funaki · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · 2006
In speech recognition, LPC cepstrum based on LPC or MFCC based on Mel-frequency filter bank are widely used as a feature extraction that determines the performance. However, these are not being regarded as the best feature extraction. In this paper, we introduce a complex speech analysis for an analytic speech signal to HMM speech recognition. A complex speech analysis can estimate more accurate speech spectrum in low frequencies, as a result, it is expected that the speech analysis can perform well as a feature extractor in speech recognition. The MMSE-based time-varying complex AR speech analysis is adopted and the estimated complex parameters are converted to LPCCs and MFCCs as a feature vector for HTK (HMM tool kit) in order to realize the HMM speech recognition. Through continuous speech recognition experiments with the converted LPCCs and MFCCs, it was found that the complex speech analysis method would not perform well than the real one