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This new algorithm contributes to better modelling of session variability in the total factor space.
A new low-dimensional space named total factor space is defined.
Each utterance is projected onto a total factor space and is represented by a low-dimensional feature vector.
Each utterance is projected onto total factor space and is represented by a low-dimensional feature vector.
A graphical explanation will be given to show that although total factor space approach is effective to compensate phonetic variability, it is still not an optimal method.
Proposed by N. Dehak, in the i-vector framework, no separate modeling of speaker and channel space is made, a single space named total factor space is constructed to model speaker and channel variability jointly.
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Based on TV language recognition system [12, 15], we propose MTV language recognition system where we use language-dependent GMMs instead of universal background model (UBM) in the process of language total variability space estimation and total factor vector extraction.
Also, the contextual information is clustered through a soft decision tree structure with the total number of L leaves partitioning the contextual factor space through the membership functions.
where mubm is the UBM supervector, T is total variability space, and the member of the vector w is total factor.
Then, the total factor vector is extracted by projecting an utterance to the language total variability space.
We can consider the total factor vector model as a new feature extractor that projects an utterance to a low rank space T to get a language and channel variability dependent total factor vector w.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com