Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td align="left">Number of hidden units</td><td align="center">Accuracy</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td align="left">100</td><td align="center">82.2</td></tr><tr><td align="left">150</td><td align="center">85.6</td></tr><tr><td align="left">200</td><td align="center">87.6</td></tr><tr><td align="left">250</td><td align="center">87.9</td></tr><tr><td align="left">300</td><td align="center">87.9</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

The tuning result of MLP attribute detectors with the number of hidden units.

Mathematical Problems in Engineering

tab2

Table 2

Table 2: Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features 