Post-Training Embedding Alignment for Decoupling Enrollment and Runtime Speaker Recognition Models
Automated speaker identification (SID) is a crucial step for the personalization of a wide range of speech-enabled services. Typical SID systems use a symmetric enrollment-verification framework with a single model to derive embeddings both offline for voice profiles extracted from enrollment utterances, and online from runtime utterances. Due to the distinct circumstances of enrollment and runtime, such as different computation and latency constraints, several applications would benefit from an asymmetric enrollment-verification framework that uses different models for enrollment and runtime embedding generation. To support this asymmetric SID where each of the two models can be updated independently, we propose using a lightweight neural network to map the embeddings from the two independent models to a shared speaker embedding space. Our results show that this approach significantly outperforms cosine scoring in a shared speaker logit space for models that were trained with a contrastive loss on large datasets with many speaker identities. This proposed Neural Embedding Speaker Space Alignment (NESSA) combined with an asymmetric update of only one of the models delivers at least 60% of the performance gain achieved by updating both models in the standard symmetric SID approach.
Code (0)
등록된 구현이 없습니다.
Tasks
Speaker IdentificationSpeaker RecognitionSimilar Papers 제목 키워드 기반
Database-Agnostic Gait Enrollment using SetTransformers
Gait recognition has emerged as a powerful tool for unobtrusive and long-range identity analysis, with growing relevance in surveillance and monitoring applications. Although recent advances in deep learning and large-sc…
Gait RecognitionSupervised and Unsupervised Alignments for Spoofing Behavioral Biometrics
Biometric recognition systems are security systems based on intrinsic properties of their users, usually encoded in high dimension representations called embeddings, which potential theft would represent a greater threat…
Reconstruction AttackSpeaker VerificationQuantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation
Single channel target speaker separation (TSS) aims at extracting a speaker's voice from a mixture of multiple talkers given an enrollment utterance of that speaker. A typical deep learning TSS framework consists of an u…
Speaker IdentificationSpeaker SeparationDiverse Semantics-Guided Feature Alignment and Decoupling for Visible-Infrared Person Re-Identification
Visible-Infrared Person Re-Identification (VI-ReID) is a challenging task due to the large modality discrepancy between visible and infrared images, which complicates the alignment of their features into a suitable commo…
Person Re-IdentificationOn the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training
Post-training of large language models routinely interleaves supervised fine-tuning (SFT) with reinforcement learning (RL). These two methods have different objectives: SFT minimizes the cross-entropy loss between model …
Reinforcement Learning