10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Weighted Linear Prediction for Speech Analysis in Noisy Conditions

Jouni Pohjalainen, Heikki Kallasjoki, Kalle J. Palomäki, Mikko Kurimo, Paavo Alku

Helsinki University of Technology, Finland

Following earlier work, we modify linear predictive (LP) speech analysis by including temporal weighting of the squared prediction error in the model optimization. In order to focus this so called weighted LP model on the least noisy signal regions in the presence of stationary additive noise, we use short-time signal energy as the weighting function. We compare the noisy spectrum analysis performance of weighted LP and its recently proposed variant, the latter guaranteed to produce stable synthesis models. As a practical test case, we use automatic speech recognition to verify that the weighted LP methods improve upon the conventional FFT and LP methods by making spectrum estimates less prone to corruption by additive noise.

Full Paper

Bibliographic reference.  Pohjalainen, Jouni / Kallasjoki, Heikki / Palomäki, Kalle J. / Kurimo, Mikko / Alku, Paavo (2009): "Weighted linear prediction for speech analysis in noisy conditions", In INTERSPEECH-2009, 1315-1318.