DAME: Duration-Aware Matryoshka Embedding for Duration-Robust Speaker Verification
Abstract
Short-utterance speaker verification remains challenging due to limited speaker-discriminative cues in short speech segments. While existing methods focus on enhancing speaker encoders, the embedding learning strategy still forces a single fixed-dimensional representation reused for utterances of any length, leaving capacity misaligned with the information available at different durations. We propose Duration-Aware Matryoshka Embedding (DAME), a model-agnostic framework that builds a nested hierarchy of sub-embeddings aligned to utterance durations: lower-dimensional representations capture compact speaker traits from short utterances, while higher dimensions encode richer details from longer speech. DAME supports both training from scratch and fine-tuning, and serves as a direct alternative to conventional large-margin fine-tuning, consistently improving performance across durations. On the VoxCeleb1-O/E/H and VOiCES evaluation sets, DAME consistently reduces the equal error rate on 1-s and other short-duration trials, while maintaining full-length performance with no additional inference cost. These gains generalize across various speaker encoder architectures under both general training and fine-tuning setups.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents DAME, a novel framework that enhances speaker verification by aligning embedding capacity with utterance duration, significantly improving performance on short utterances. This contribution is particularly relevant in the context of real-world applications where short speech segments are common, addressing a critical limitation in existing speaker verification methodologies.
Methodology
The proposed DAME framework introduces a novel approach to speaker verification by creating a nested hierarchy of sub-embeddings that are aligned with utterance durations. This model-agnostic strategy allows for dynamic adjustment of embedding sizes based on the length of the input, addressing a significant gap in existing methods that utilize fixed-dimensional representations. The methodology is well-structured, leveraging duration-aware supervision to enhance the discriminative power of embeddings for both short and long utterances. The use of prefix embeddings and a multi-prefix loss function demonstrates a thoughtful integration of existing concepts in representation learning while innovatively adapting them to the specific challenges of speaker verification.
Results & evaluation
The experiments are comprehensive, utilizing multiple datasets (VoxCeleb1-O/E/H and VOiCES) to evaluate the performance of DAME across various speaker encoder architectures. The results show consistent improvements in equal error rates (EER) for short-duration trials, which is a critical metric in speaker verification. The paper effectively compares DAME against conventional methods, including large-margin fine-tuning, and demonstrates its robustness across different conditions. However, the paper could benefit from more extensive ablation studies to further elucidate the contributions of individual components of the DAME framework.
Reproducibility
The paper provides sufficient details regarding the training regime, model configurations, and evaluation protocols, which would allow other researchers to replicate the experiments. However, the absence of a publicly available code repository or demo URL limits the ease of reproducibility. Including such resources would significantly enhance the paper's impact and facilitate further research in this area.
Limitations
While the DAME framework shows promise, it may still face challenges in extreme real-world scenarios where utterance quality is poor or heavily distorted. Additionally, the reliance on specific datasets may limit the generalizability of the findings. The paper does not address potential biases in the datasets used, which could affect the model's performance across diverse populations.
Broader impact
The advancements presented in this paper have significant implications for applications in security, telecommunications, and human-computer interaction, where robust speaker verification is essential. By improving the performance of speaker verification systems on short utterances, DAME could enhance user experience in voice-activated systems and contribute to more secure authentication methods. The paper presents DAME, a novel framework that enhances speaker verification by aligning embedding capacity with utterance duration, significantly improving performance on short utterances. This contribution is particularly relevant in the context of real-world applications where short speech segments are common, addressing a critical limitation in existing speaker verification methodologies.
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