System Combination for Short Utterance Speaker Recognition

TL;DR

This paper introduces a combined phonetic-aware system for short-utterance speaker recognition, improving accuracy by integrating a DNN-based i-vector approach and a subregion GMM-UBM system, demonstrating enhanced performance over individual models.

Contribution

The paper proposes a novel score-level fusion of two phonetic-aware systems, enhancing short-utterance speaker recognition accuracy beyond existing methods.

Findings

Both systems outperform their baselines.

Fusion of systems yields further performance gains.

Approach is effective for text-independent SUSR.

Abstract

For text-independent short-utterance speaker recognition (SUSR), the performance often degrades dramatically. This paper presents a combination approach to the SUSR tasks with two phonetic-aware systems: one is the DNN-based i-vector system and the other is our recently proposed subregion-based GMM-UBM system. The former employs phone posteriors to construct an i-vector model in which the shared statistics offers stronger robustness against limited test data, while the latter establishes a phone-dependent GMM-UBM system which represents speaker characteristics with more details. A score-level fusion is implemented to integrate the respective advantages from the two systems. Experimental results show that for the text-independent SUSR task, both the DNN-based i-vector system and the subregion-based GMM-UBM system outperform their respective baselines, and the score-level system combination delivers performance improvement.