This paper proposes an improved discrete Fourier transform (DFT)-based channel estimation technique for time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) communication systems. The proposed technique, based on the concept of significant channel tap detector (SCTD) scheme, can effectively improve the system performance of TDS-OFDM systems. The correlation of two successive preambles is employed to estimate the average noise power as the threshold for obtaining the SCTD threshold estimation error and loss path information in large delay spread channel environments. The proposed estimation scheme roughly predicts the noise power in order to choose the significant channel taps to estimate the channel impulse response. Some comparative simulations are given to show that the proposed technique has the potential to achieve bit error rate performance superior to that of the conventional least squares channel estimation.

This paper proposes several cepstral statistics compensation and normalization algorithms which alleviate the effect of additive noise on cepstral features for speech recognition. The algorithms are simple yet efficient noise reduction techniques that use online-constructed pseudo-stereo codebooks to evaluate the statistics in both clean and noisy environments. The process yields transformations for both clean speech cepstra and noise-corrupted speech cepstra, or for noise-corrupted speech cepstra only, so that the statistics of the transformed speech cepstra are similar for both environments. Experimental results show that these codebook-based algorithms can provide significant performance gains compared to results obtained by using conventional utterance-based normalization approaches. The proposed codebook-based cesptral mean and variance normalization (C-CMVN), linear least squares (LLS) and quadratic least squares (QLS) outperform utterance-based CMVN (U-CMVN) by 26.03%, 22.72% and 27.48%, respectively, in relative word error rate reduction for experiments conducted on Test Set A of the Aurora-2 digit database.