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

AARON LAWSON; MITCH MCLAREN; YUN LEI; VIKRAMJIT MITRA; NICOLAS SCHEFFER; LUCIANA FERRER; MARTIN GRACIARENA

Vancouver

Congreso; IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2013

IEEE

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 cal- ibration. 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.