Regularization of neural network model with distance metric learning for i-vector based spoken language identification
The i-vector representation and modeling technique has been successfully applied in
spoken language identification (SLI). The advantage of using the i-vector representation is
that any speech utterance with a variable duration length can be represented as a fixed
length vector. In modeling, a discriminative transform or classifier must be applied to
emphasize the variations correlated to language identity since the i-vector representation
encodes several types of the acoustic variations (eg, speaker variation, transmission …
spoken language identification (SLI). The advantage of using the i-vector representation is
that any speech utterance with a variable duration length can be represented as a fixed
length vector. In modeling, a discriminative transform or classifier must be applied to
emphasize the variations correlated to language identity since the i-vector representation
encodes several types of the acoustic variations (eg, speaker variation, transmission …
[PDF][PDF] Regularization of neural network model with distance metric learning for i-vector based spoken language identification
XLP Shen, Y Tsao, H Kawai - researchgate.net
The i-vector representation and modeling technique has been successfully applied in
spoken language identification (SLI). The advantage of using the i-vector representation is
that any speech utterance with a variable duration length can be represented as a fixed
length vector. In modeling, a discriminative transform or classifier must be applied to
emphasize the variations correlated to language identity since the i-vector representation
encodes several types of the acoustic variations (eg, speaker variation, transmission …
spoken language identification (SLI). The advantage of using the i-vector representation is
that any speech utterance with a variable duration length can be represented as a fixed
length vector. In modeling, a discriminative transform or classifier must be applied to
emphasize the variations correlated to language identity since the i-vector representation
encodes several types of the acoustic variations (eg, speaker variation, transmission …
Showing the best results for this search. See all results