Noise Detection and Classification in Speech Signals with Boosting

This paper presents a novel method to detect and classify sudden noises in speech signals. There are many sudden and short-period noises in natural environments, such as inside a car. If a speech recognition system can detect sudden noises, it will make it possible for the system to ask the speaker to repeat the same utterance so that the speech data will be clean. If clean speech data can be input, it will help prevent system operation errors. In this paper, we tried to detect and classify sudden noises in user´s utterances using Boosting. Boosting can create a complex, non-linear boundary that determines whether the observed signal is speech, noise1, noise2, or so on. In our experiments, the proposed method achieved good performance in comparison to a conventional method based on the GMM (Gaussian Mixture Model).