Deep temporal representation learning for language identification.

Area of Science:

• Speech processing
• Machine learning
• Computational linguistics

Background:

• Language identification (LID) is crucial for many speech processing applications.
• Wav2Vec 2.0 (W2V2) provides effective frame-level speech representations but lacks temporal information extraction for LID.
• Existing LID methods struggle to effectively leverage temporal dynamics from frame-level features.

Purpose of the Study:

• To propose a novel LID framework, Deep Temporal Representation (DTR) learning, to enhance performance by capturing temporal dependencies.
• To integrate W2V2's contextual speech representations with a dedicated temporal network for utterance-level feature extraction.
• To improve the accuracy and robustness of language identification systems.

Main Methods:

• Utilized Wav2Vec 2.0 as a front-end feature extractor for contextual speech representations.
• Developed a temporal network comprising a temporal representation extractor and a temporal regularization term.
• Extracted utterance-level representations by learning temporal dependencies from W2V2 features.

Main Results:

• The proposed DTR method demonstrated effective extraction of temporal information from speech features.
• Evaluated on the OLR2020 database, DTR achieved competitive experimental performance across all three tasks.
• The framework successfully enhanced LID performance by incorporating deep temporal representations.

Conclusions:

• Deep Temporal Representation (DTR) learning is a viable approach for improving language identification systems.
• Integrating temporal dynamics learning with pre-trained speech models like W2V2 offers significant benefits for LID.
• The proposed method provides a robust solution for extracting and utilizing temporal information in speech classification tasks.