Model adaptation in line spectrum domain

Line spectrum frequency (LSF) is an efficient and reliable representation for speech signals. This property makes LSFs popular in low bit-rate speech coding. However, the use of LSF parameters in speech recognition still faces the problem of performance degradation in a noisy environment. This paper investigates the noise effect in the line spectrum domain and presents a model adaptation method for the improvement of noisy speech recognition. It comes out an interpolation mechanism in the line spectrum domain so that clean speech models can be adjusted according to the LSF of the noise signal. An experiment on Mandarin digit recognition has demonstrated the effectiveness and robustness of our proposed method