tailieunhanh - Advanced Digital Signal Processing and Noise Reduction P2

Linear Prediction Modelling of Speech Linear predictive models are widely used in speech processing applications such as low–bit–rate speech coding in cellular telephony, speech enhancement and speech recognition. Speech is generated by inhaling air into the lungs, and then exhaling it through the vibrating glottis cords and the vocal tract. The random, noise-like, air flow from the lungs is spectrally shaped and amplified by the vibrations of the glottal cords and the resonance of the vocal tract. The effect of the vibrations of the glottal cords and the vocal tract is to introduce a measure of correlation and predictability. | Applications of Digital Signal Processing 11 acoustic speech feature sequence representing an unlabelled spoken word as one of the V likely words or silence. For each candidate word the classifier calculates a probability score and selects the word with the highest score. Linear Prediction Modelling of Speech Linear predictive models are widely used in speech processing applications such as low-bit-rate speech coding in cellular telephony speech enhancement and speech recognition. Speech is generated by inhaling air into the lungs and then exhaling it through the vibrating glottis cords and the vocal tract. The random noise-like air flow from the lungs is spectrally shaped and amplified by the vibrations of the glottal cords and the resonance of the vocal tract. The effect of the vibrations of the glottal cords and the vocal tract is to introduce a measure of correlation and predictability on the random variations of the air from the lungs. Figure illustrates a model for speech production. The source models the lung and emits a random excitation signal which is filtered first by a pitch filter model of the glottal cords and then by a model of the vocal tract. The main source of correlation in speech is the vocal tract modelled by a linear predictor. A linear predictor forecasts the amplitude of the signal at time m x m using a linear combination of P previous samples x m -1 --- x m - P as P x m akx m - k k 1 where x m is the prediction of the signal x m and the vector aT a . aP is the coefficients vector of a predictor of order P. The Pitch period Figure Linear predictive model of speech. 12 Introduction Figure Illustration of a signal generated by an all-pole linear prediction model. prediction error e m . the difference between the actual sample x m and its predicted value X m is defined as p e m x m - akx m - k k 1 The prediction error e m may also be interpreted as the random excitation or the so-called innovation content of x m . .