Application of Convolutional Neural Networks to Language Identification in Noisy Conditions

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Citation

Lei, Y., Ferrer, L., Lawson, A., McLaren, M., & Scheffer, N. (2014, June). Application of Convolutional Neural Networks to Language Identification in Noisy Conditions. In Odyssey.

Abstract

This paper proposes two novel frontends for robust language identification (LID) using a convolutional neural network (CNN) trained for automatic speech recognition (ASR).  In the CNN/i-vector frontend, the CNN is used to obtain the posterior probabilities for i-vector training and extraction instead of a universal background model (UBM).  The CNN/posterior frontend is somewhat similar to a phonetic system in that the occupation counts of (tied) triphone states (senones) given by the CNN are used for classification.  They are compressed to a low dimensional vector using probabilistic principal component analysis (PPCA). Evaluated on heavily degraded speech data, the proposed front ends provide significant improvements of up to 50% on average equal error rate compared to a UBM/i-vector baseline.  Moreover, the proposed frontends are complementary and give significant gains of up to 20% relative to the best single system when combined.


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