Deep joint learning for language recognition

Area of Science:

• Speech Processing
• Machine Learning
• Artificial Intelligence

Background:

• Deep learning shows promise for language recognition.
• Existing methods often use single acoustic features and tasks, limiting performance.
• There's a need for more robust language recognition systems.

Purpose of the Study:

• To propose deep joint learning strategies using Multi-Feature (MF) and Multi-Task (MT) models.
• To investigate integrating multiple acoustic features and novel training constraints.
• To enhance language recognition performance, especially in complex scenarios.

Main Methods:

• Developed deep joint learning strategies with MF and MT models.
• Incorporated auxiliary classification constraints and Canonical Correlation Analysis (CCA).
• Utilized correlated speech tasks like phoneme recognition as auxiliary tasks.

Main Results:

• Joint learning on MF and MT models effectively extracts language identity features.
• Proposed methods significantly improve language recognition performance.
• Performance gains are notable in challenging cross-channel and open-set conditions.

Conclusions:

• Deep joint learning with MF and MT models offers a powerful approach for language recognition.
• Integrating multiple features and tasks enhances robustness.
• The Language-Phoneme embedding extraction structure aids simultaneous learning.