Improving Language Identification Robustness to Highly Channel-Degraded Speech through Multiple System Fusion

Abstract

We describe a language identification system developed for robustess to noise conditions such as those encountered under the DARPA RATS program, which is focused on multi-channel audio collected in high noise conditions. Work presented here includes novel approaches to scoring iVectors, the introduction of several new acoustic and prosodic features for language identification, and discriminative file selection approaches to score calibration.  Further, we explore the use of Discrete Cosine Transforms (DCT) as a supplement to traditional context modeling with Shifted Delta Cepstrum (SDC) and fusion of multiple iVector systems based on Gaussian backends, neural networks, and adaptive Gaussian backend modeling.

Index Terms: language identification, speech features, iVector scoring.