Speech processing for under-resourced languages is an active field of research, which has
experienced significant progress during the past decade. We propose, in this paper, a …

Arabic automatic speech recognition (ASR) methods with diacritics have the ability to be
integrated with other systems better than Arabic ASR methods without diacritics. In this work …

This work presents an algorithm for the unsupervised learning, or induction, of a simple
morphology of a natural language. A probabilistic maximum a posteriori model is utilized …

In the speech recognition of highly inflecting or compounding languages, the traditional
word-based language modeling is problematic. As the number of distinct word forms can …

We present a new type of neural probabilistic language model that learns a map** from
both words and explicit word features into a continuous space that is then used for word …

Recent results with phone-posterior acoustic features estimated by multilayer perceptrons
(MLPs) have shown that such features can effectively improve the accuracy of state-of-the …

Part of Speech tagging for Indian Languages in general and Hindi in particular is not a very
widely explored territory. There have been many attempts at develo** a good POS tagger …

We present the first comprehensive study of Native Language Identification (NLI) applied to
text written in languages other than English, using data from six languages. NLI is the task of …

Recurrent neural networks are convenient and efficient models for language modeling.
However, when applied on the level of characters instead of words, they suffer from several …

Named entity recognition has served many natural language processing tasks such as
information retrieval, machine translation, and question answering systems. Many …