Language Identification using Acoustic Models and Speaker Compensated Cepstral-Time Matrices

This work presents two contributions to language identification. The first contribution is the definition of a set of properly selected time-frequency features that are a valid alternative to the commonly used shifted delta cepstral features. As a second contribution, we show that significant performance improvement in language recognition can be obtained estimating a subspace that represents the distortions due to inter-speaker variability within the same language, and compensating these distortions in the domain of the features. Experiments on the NIST 1996 and 2003 Language Recognition Evaluation data have been successfully used to validate the effectiveness of the proposed techniques.