Noise Robust Feature Extraction Based on Extended Weighted Linear Prediction in LVCSR

Reference:

Sami Keronen, Jouni Pohjalainen, Paavo Alku, and Mikko Kurimo. Noise robust feature extraction based on extended weighted linear prediction in lvcsr. In Proceedings of INTERSPEECH 2011, Florence, Italy, August 2011.

Abstract:

This paper introduces extended weighted linear prediction (XLP) to noise robust short-time spectrum analysis in the feature extraction process of a speech recognition system. XLP is a generalization of standard linear prediction (LP) and temporally weighted linear prediction (WLP) which have already been applied to noise robust speech recognition with good results. With XLP, higher controllability to the temporal weighting of different parts of the noisy speech is gained by taking the lags of the signal into account in prediction. Here, the performance of XLP is put up against WLP and conventional spectrum analysis methods FFT and LP on a large vocabulary continuous speech recognition (LVCSR) scheme using real world noisy data containing additive and convolutive noise. The results show improvements over the reference methods in several cases.

Keywords:

linear prediction, temporal weighting, noise robust, speech recognition

Suggested BibTeX entry:

@inproceedings{keronen11.is,
    address = {Florence, Italy},
    author = {Sami Keronen and Jouni Pohjalainen and Paavo Alku and Mikko Kurimo},
    booktitle = {Proceedings of INTERSPEECH 2011},
    language = {eng},
    month = {August},
    title = {Noise Robust Feature Extraction Based on Extended Weighted Linear Prediction in LVCSR},
    year = {2011},
}