We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...
Multi-task learning aims at combining information across tasks to boost prediction performance, especially when the number of training samples is small and the number of predictor...
This paper investigates syntactic and sub-lexical features in Turkish discriminative language models (DLMs). DLM is a featurebased language modeling approach. It reranks the ASR o...
Ebru Arisoy, Murat Saraclar, Brian Roark, Izhak Sh...
We present an approximation to the Bayesian hierarchical PitmanYor process language model which maintains the power law distribution over word tokens, while not requiring a comput...
The standard approach to speaker verification is to extract cepstral features from the speech spectrum and model them by generative or discriminative techniques. We propose a nov...