2 research outputs found
Sparse Architectures for Text-Independent Speaker Verification Using Deep Neural Networks
Network pruning is of great importance due to the elimination of the
unimportant weights or features activated due to the network
over-parametrization. Advantages of sparsity enforcement include preventing the
overfitting and speedup. Considering a large number of parameters in deep
architectures, network compression becomes of critical importance due to the
required huge amount of computational power. In this work, we impose structured
sparsity for speaker verification which is the validation of the query speaker
compared to the speaker gallery. We will show that the mere sparsity
enforcement can improve the verification results due to the possible initial
overfitting in the network
Subword-Level Language Identification for Intra-Word Code-Switching
Language identification for code-switching (CS), the phenomenon of
alternating between two or more languages in conversations, has traditionally
been approached under the assumption of a single language per token. However,
if at least one language is morphologically rich, a large number of words can
be composed of morphemes from more than one language (intra-word CS). In this
paper, we extend the language identification task to the subword-level, such
that it includes splitting mixed words while tagging each part with a language
ID. We further propose a model for this task, which is based on a segmental
recurrent neural network. In experiments on a new Spanish--Wixarika dataset and
on an adapted German--Turkish dataset, our proposed model performs slightly
better than or roughly on par with our best baseline, respectively. Considering
only mixed words, however, it strongly outperforms all baselines.Comment: NAACL-HLT 201