Language Normalization for Bilingual Speaker Recognition Systems

Abstract

In this study, we focus on the problem of removing/normalizing the impact of spoken language variation in bilingual speaker recognition (BSR) systems. In addition to environment, recording, and channel mismatches, spoken language mismatch is an additional factor resulting in performance degradation in speaker recognition systems. In today's world, the number of bilingual speakers is increasing with English becoming the universal second language. Data sparseness is becoming an important research issue to deploy speaker recognition systems with limited resources (e.g., short train/test durations). Therefore, leveraging existing resources from different languages becomes a practical concern in limited-resource BSR applications, and effective language normalization schemes are required to achieve more robust speaker recognition systems. Here, we propose two novel algorithms to address the spoken language mismatch problem: normalization at the utterance-level via language identification (LID), and normalization at the segment-level via multilingual phone recognition (PR). We evaluated our algorithms using a bilingual (Spanish-English) speaker set of 80 speakers. Experimental results show improvements over a baseline system which employs fusion of language-dependent speaker models with fixed weights.