Speech recognition under additive noise

In this paper we present a study on the performance of a speaker-dependent continuous speech recognition algorithm in various background noise levels, including the mismatch tolerance of the algorithm. This mismatch exists in most applications where the user is trained in one noise level and does the recognition in different and highly variable noise levels. Finally, we introduce a pre-processing technique called ´twicing´ and a simple 3-point moving average post-processor. The twicing algorithm, while still maintaining the high performance in the quiet background, reduces the error rate in the noisy environments by a significant amount. The results indicate that smoothing in addition to twicing provides significant performance improvement in high noise background.