A comparative study of cepstral lifters and distance measures for all pole models of speech in noise

After a brief overview of the techniques utilized, the authors evaluate perceptually based linear prediction (PLP) analysis, and then report the results of a comparative study of several front-ends in the case of speech produced in quiet and noisy environments (Lombard effect). Several all-pole models of speech using various lifters and distance measures are compared in various noise conditions. The main conclusions of this research are: (1) when speech is produced in a quiet environment and in speaker-dependent automatic speech recognition (ASR), the cepstral projection measure significantly improves recognition scores for the three all-pole models considered (for clean reference and noisy test templates), with the best results obtained with the LP analysis (for SNR=5 dB); (2) when speech is produced in a quiet environment and in speaker-dependent and cross-speaker ASR, the optimal filter is a function of the SNR of the test and the reference templates; and (3) when speech is produced in noise and in speaker-dependent ASR, the PLPRPS front-end is the best, and a low model order of the analysis is suitable