NEAREST NEIGHBOR DISCRIMINANT ANALYSIS FOR LANGUAGE RECOGNITION

Many state-of-the-art i-vector based voice biometric systems use lin-ear discriminant analysis (LDA) as a post-processing stage to in-crease the computational efficiency in the back-end via dimension-ality reduction, as well as annihilate the undesired (noisy) directions in the total variability subspace. The traditional approach for com-puting the LDA transform uses parametric representations for both intra- and inter-class scatter matrices that are based on the Gaussian distribution assumption. However, it is known that the actual distri-bution of i-vectors may not necessarily be Gaussian, and in particu-lar, in the presence of noise and channel distortions. In addition, the rank of the LDA projection (i.e., the maximum number of available discriminant bases) is limited to the number of classes minus 1. Ac-cordingly, language recognition tasks on noisy data that involve only a few language classes receive limited or no benefit from the LDA post-processing. Motivated by this observation, we present an alter-native non-parametric discriminant analysis (NDA) technique that measures both the within- and between-language variation on a lo-cal basis using the nearest neighbor rule. The effectiveness of the NDA method is evaluated in the context of noisy language recogni-tion tasks using speech material from the DARPA Robust Automatic Transcription of Speech (RATS) program. Experimental results indi-cate that NDA is more effective than the traditional parametric LDA for language recognition under noisy and channel degraded condi-tions. Index Terms — RATS, language recognition, nearest neighbor, discriminant analysis, i-vecto