Improved Algorithms for Linear Prediction Speech

• Main Authors: Hsiang-Hao Hsieh, 謝祥浩
• Other Authors: Soo-Chang Pei
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/19500387046243935558

碩士 === 國立臺灣大學 === 電信工程學研究所 === 100 === We observe from the waveform of speech signal, and we can find out that in the period of voiced speech, the amplitude of one sample has a relationship with its neighbors. Therefore, we can estimate someone sample by taking previous other samples. And then, this result is the conception of linear prediction. In digital speech processing, linear prediction is a very important method of analysis, and we usually use linear prediction to get features in the speech signal in fact. By modeling the spectral envelope, linear prediction can capture the most essential acoustical cues of speech originating from two major parts of the human voice production mechanism, the glottal flow and the vocal tract. However, linear prediction analysis also suffers from some drawbacks, for examples, the biasing of the formant estimates by their neighboring harmonics which caused by aliasing that occurs in the autocorrelation domain and the phenomenon is most severe for high-pitch speaker in general. Additionally, it is well-known that the performance of LP deteriorates in the presence of noise. In this thesis, we try to improve conventional algorithm to solve these problems, in order to make spectral envelope estimation more accuracy and increase robustness against noise. By our verification, these improved algorithms all have the stability of the all-pole filters.