Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs (2004.02863v5)

Abstract: In practical settings, a speaker recognition system needs to identify a speaker given a short utterance, while the enroLLMent utterance may be relatively long. However, existing speaker recognition models perform poorly with such short utterances. To solve this problem, we introduce a meta-learning framework for imbalance length pairs. Specifically, we use a Prototypical Networks and train it with a support set of long utterances and a query set of short utterances of varying lengths. Further, since optimizing only for the classes in the given episode may be insufficient for learning discriminative embeddings for unseen classes, we additionally enforce the model to classify both the support and the query set against the entire set of classes in the training set. By combining these two learning schemes, our model outperforms existing state-of-the-art speaker verification models learned with a standard supervised learning framework on short utterance (1-2 seconds) on the VoxCeleb datasets. We also validate our proposed model for unseen speaker identification, on which it also achieves significant performance gains over the existing approaches. The codes are available at https://github.com/seongmin-kye/meta-SR.

• The paper introduces a meta-learning framework utilizing Prototypical Networks to address the challenge of imbalanced utterance lengths in short utterance speaker recognition.
• A novel dual-scheme training objective combines episodic and global classification loss to learn discriminative and length-consistent speaker embeddings.
• Empirical evaluation on VoxCeleb datasets demonstrates superior performance over traditional methods, significantly reducing Equal Error Rates for short utterance verification.

Meta-Learning for Short Utterance Speaker Recognition

The paper "Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs" explores a meta-learning approach to enhance speaker recognition systems, particularly when faced with the considerable challenge of short utterance verification. In many practical scenarios, test utterances are significantly shorter than enroLLMent utterances, which introduces a discrepancy impacting the efficacy of conventional speaker recognition models.

Methodological Innovations

The research introduces a meta-learning framework employing Prototypical Networks to address the imbalance in utterance lengths between training and inference. The framework organizes each training episode with a support set comprising long utterances and a query set of short utterances of varying lengths. This structure aims to simulate real-world conditions and train a model that remains robust across different utterance durations.

A pivotal advancement is the dual-scheme training objective combining episodic classification loss with global classification loss across the entire training dataset. The model learns to generate embeddings that are both discriminative to unseen data and consistent across varied utterance lengths. The global classification ensures that embeddings from utterances of different lengths are coherent, thereby improving recognition accuracy.

Empirical Evaluation

To verify the proposed framework, the authors employed ResNet34 as the backbone network architecture, which is a common choice in speaker recognition tasks due to its robust feature extraction capabilities. The model was evaluated against state-of-the-art systems using the VoxCeleb1 and VoxCeleb2 datasets, a benchmark for speaker recognition tasks that provides ample scope for validation across both short and long utterances.

The model demonstrated superior performance over traditional speaker recognition frameworks across several evaluations: short utterance verification, unseen speaker identification, and full-duration speaker verification. Specifically, in short utterance cases (1-2 seconds), it showed a marked improvement in Equal Error Rates (EER), a critical metric in verification tasks.

Theoretical and Practical Implications

Theoretically, this work advances the understanding of meta-learning applications within the domain of speaker recognition, providing an architectural blueprint that could be extended to other domains with similar constraints on input size variability. Practical implications are manifest in its potential application in real-world settings where short utterance verification is common, for instance in security systems and virtual assistants where quick, accurate user identification is crucial.

Future Directions

Future developments based on this research could further optimize the meta-learning scheme by integrating more advanced feature aggregation techniques or experimenting with different types of meta-learning models such as relation networks. Additionally, exploring cross-language and cross-domain transfer learning could enhance the versatility of the proposed speaker recognition framework, allowing it to generate robust performance across diverse operational environments.

This research significantly contributes to the domain of speaker recognition, providing a methodological framework that improves the recognition accuracy for short utterances, an area where traditional methods often fall short. The combination of meta-learning with a length-imbalance approach represents a promising avenue for future research in speaker recognition and related domains.