Speaker verification using acoustic factor analysis with phonetic content compensation in limited and degraded test conditions

This work explores speaker verification (SV) under limited and degraded test conditions from the perspective of practical systems. An i-vector based SV system using maximum likelihood - acoustic factor analysis (ML-AFA) technique for modeling and short utterance variance normalization (SUVN) technique for enhancing the phonetic content of short utterance i-vectors is developed. As shorter utterances in text-independent scenarios may contain any phonetic content, the i-vectors for test utterances also vary significantly. SUVN helps in compensating the variation in i-vectors due to phonetic content and improves the performance in limited test data conditions. Whereas ML-AFA technique helps in reducing the effect of noise. Utilizing both these frameworks, a SV system under limited and degraded test conditions is proposed. The proposed system is found to outperform the baseline system by a significant margin. Finally, the proposed and the baseline systems are fused at scores level that further boosts the performance.