Robust digit recognition in noisy environments: the IBM Aurora 2 system
George Saon, Juan M. Huerta, Ea-Ee Jan · 2001
ABSTRACTIn this paper we describe some experiments on the Aurora 2 noisydigits database. The algorithms that we used can be broadly clas-sified into noise robustness techniques based on a linear-channelmodel of the acoustic environment such as CDCN [1] and its novelvariant termed Alignment-based CDCN( ACDCN , proposed here),and techniques which do not assume any particular knowledgeabout thestructure of the environment or noise conditions affectingthe speech signal such as discriminant feature space transforma-tions and speaker/channel adaptation. We present recognition ex-periments for both the clean training data and the multi-conditiontraining data scenarios.1. INTRODUCTIONIn this paper we describe the system and techniques for the Aurora2 noisy digits database and the results obtained. We developed twosets of acoustic models: the first set of models was trained on cleandata only and the second set was trained on the multi-conditiontraining data. For the system trained on clean data only, we appliedCDCN [1] and a novel variant of this technique called Alignment-based CDCN (