Exploring wav2vec 2.0 on speaker verification and language identification (2012.06185v2)

Abstract: Wav2vec 2.0 is a recently proposed self-supervised framework for speech representation learning. It follows a two-stage training process of pre-training and fine-tuning, and performs well in speech recognition tasks especially ultra-low resource cases. In this work, we attempt to extend self-supervised framework to speaker verification and language identification. First, we use some preliminary experiments to indicate that wav2vec 2.0 can capture the information about the speaker and language. Then we demonstrate the effectiveness of wav2vec 2.0 on the two tasks respectively. For speaker verification, we obtain a new state-of-the-art result, Equal Error Rate (EER) of 3.61% on the VoxCeleb1 dataset. For language identification, we obtain an EER of 12.02% on 1 second condition and an EER of 3.47% on full-length condition of the AP17-OLR dataset. Finally, we utilize one model to achieve the unified modeling by the multi-task learning for the two tasks.

Exploring wav2vec 2.0 for Speaker Verification and Language Identification

The paper, "Exploring wav2vec 2.0 on speaker verification and language identification," investigates the application of the self-supervised learning framework wav2vec 2.0 in tasks beyond its original scope of speech recognition, particularly in speaker verification (SV) and language identification (LID). The self-supervised framework involves a two-stage process of pre-training followed by fine-tuning. This approach, initially designed to enhance automatic speech recognition (ASR), is known for its efficiency in scarce resource scenarios. The authors explore whether this framework can effectively capture speaker and language features from audio data, a topic that has not been widely examined within the field of speech processing.

Methodology and Findings

The paper employs the architecture of wav2vec 2.0, which includes a feature encoder based on convolutional neural networks (CNNs), a Transformer network, and a quantization module. These components work together to transform raw audio waveforms into latent vectors before converting them into discrete representations. The model is trained through a contrastive loss mechanism designed to differentiate true feature representations from negative samples. Once pre-trained on unlabeled data, the model is subsequently fine-tuned for specific downstream tasks, namely SV and LID.

Speaker Verification

For the SV task, the authors fine-tuned a pre-trained w2v-encoder on the VoxCeleb1 dataset, achieving a state-of-the-art Equal Error Rate (EER) of 3.61%. This performance surpasses various established methodologies, including i-vector approaches and several neural network-based systems. A comparative model trained from scratch without pre-training yielded significantly higher error rates, affirming the efficacy of self-supervised pre-training in enhancing speaker verification systems.

Language Identification

In the context of LID, experiments were conducted using the AP17-OLR dataset. The fine-tuned model yielded an EER of 12.02% on short 1-second audio samples and 3.47% on full-length samples. Though not superior to the best results within the datasets, the findings indicate that wav2vec 2.0 can indeed be adapted for language identification, with notable advancements over models that bypassed pre-training. The results underscore the utility of self-supervised pre-training in retaining distinguishing features for multiple languages, even when pre-trained on monolingual datasets like Librispeech.

Multi-task Learning

The authors further investigated a multi-task learning scenario by jointly fine-tuning for both SV and LID. Using a single model with a shared w2v-encoder and distinct output layers for each task, they demonstrated that wav2vec 2.0 efficiently supports a unified approach to multiple speech tasks without excessive parameter inflation. Although performance on individual tasks slightly decreased compared to individual fine-tuning, this unified model strikes a balance between storage efficiency and task performance.

Implications and Future Directions

The findings from this research have significant implications for the deployment of self-supervised models in diverse speech processing tasks. The potential for reducing large-scale labeled data requirements while maintaining high performance is particularly pertinent for applications where labeled datasets are costly or scarce. In a broader scope, this paper contributes to the growing evidence on the versatility and robustness of self-supervised learning strategies in AI.

Future research may extend this framework to other aspects of speech processing, such as emotion recognition or dialect classification. Furthermore, exploring multilingual pre-training could potentiate improvements in language identification tasks, a hypothesis grounded in the current findings on the language-agnostic capabilities of wav2vec 2.0.

In conclusion, the adaptation of wav2vec 2.0 to speaker verification and language identification tasks exemplifies the transition of self-supervised learning paradigms from foundational research to practical, task-oriented applications, encouraging continued exploration in the AI community.