Language/Dialect Recognition Based on Unsupervised Deep Learning

Over the past decade, bottleneck features within an i-Vector framework have been used for state-of-the-art language/dialect identification (LID/DID). However, traditional bottleneck feature extraction requires additional transcribed speech information. Alternatively, two types of unsupervised deep learning methods are introduced in this study. To address this limitation, an unsupervised bottleneck feature extraction approach is proposed, which is derived from the traditional bottleneck structure but trained with estimated phonetic labels. In addition, based on a generative modeling autoencoder, two types of latent variable learning algorithms are introduced for speech feature processing, which have been previous considered for image processing/reconstruction. Specifically, a variational autoencoder and adversarial autoencoder are utilized on alternative phase of speech processing. To demonstrate the effectiveness of the proposed methods, three corpora are evaluated: 1) a four Chinese dialect dataset, 2) a five Arabic dialect corpus, and 3) multigenre broadcast challenge corpus (MGB-3) for arabic DID. The proposed features are shown to outperform traditional acoustic feature MFCCs consistently across three corpora. Taken collectively, the proposed features achieve up to a relative +58% improvement in $C_{\text{avg}}$ for LID/DID without the need of any secondary speech corpora.