Insights into End-to-End Learning Scheme for Language Identification
This work addresses language identification for speech processing applications, offering an incremental improvement over existing methods.
The authors tackled language identification by proposing an interpretable end-to-end learning scheme that integrates a general encoding layer with a CNN front-end, achieving state-of-the-art performance on the NIST LRE07 closed-set task.
A novel interpretable end-to-end learning scheme for language identification is proposed. It is in line with the classical GMM i-vector methods both theoretically and practically. In the end-to-end pipeline, a general encoding layer is employed on top of the front-end CNN, so that it can encode the variable-length input sequence into an utterance level vector automatically. After comparing with the state-of-the-art GMM i-vector methods, we give insights into CNN, and reveal its role and effect in the whole pipeline. We further introduce a general encoding layer, illustrating the reason why they might be appropriate for language identification. We elaborate on several typical encoding layers, including a temporal average pooling layer, a recurrent encoding layer and a novel learnable dictionary encoding layer. We conducted experiment on NIST LRE07 closed-set task, and the results show that our proposed end-to-end systems achieve state-of-the-art performance.