ISCA Archive Interspeech 2015
ISCA Archive Interspeech 2015

Exploiting i-vector posterior covariances for short-duration language recognition

Sandro Cumani, Oldřich Plchot, Radek Fér

Linear models in i-vector space have shown to be an effective solution not only for speaker identification, but also for language recognition. The i-vector extraction process, however, is affected by several factors, such as noise level, the acoustic content of the utterance and the duration of the spoken segments. These factors influence both the i-vector estimate and its uncertainty, represented by the i-vector posterior covariance matrix. Modeling of i-vector uncertainty with Probabilistic Linear Discriminant Analysis has shown to be effective for short-duration speaker identification. This paper extends the approach to language recognition, analyzing the effects of i-vector covariances on a state-of-the-art Gaussian classifier, and proposes an effective solution for the reduction of the average detection cost (Cavg) for short segments.

doi: 10.21437/Interspeech.2015-273

Cite as: Cumani, S., Plchot, O., Fér, R. (2015) Exploiting i-vector posterior covariances for short-duration language recognition. Proc. Interspeech 2015, 1002-1006, doi: 10.21437/Interspeech.2015-273

  author={Sandro Cumani and Oldřich Plchot and Radek Fér},
  title={{Exploiting i-vector posterior covariances for short-duration language recognition}},
  booktitle={Proc. Interspeech 2015},