Robust digit recognition in noisy environments: The IBM aurora 2 system

Abstract

In this paper we describe some experiments on the Aurora 2 noisy digits database. The algorithms that we used can be broadly classified into noise robustness techniques based on a linear-channel model of the acoustic environment such as CDCN [1] and its novel variant termed Alignment-based CDCN (ACDCN, proposed here), and techniques which do not assume any particular knowledge about the structure of the environment or noise conditions affecting the speech signal such as discriminant feature space transformations and speaker/channel adaptation. We present recognition experiments for both the clean training data and the multi-condition training data scenarios.