ABSTRACT Automatic Speech Recognition (ASR) is an active field of research due to its
large number of applications and the proliferation of interfaces or computing devices that …

This article surveys work on Unsupervised Learning of Morphology. We define
Unsupervised Learning of Morphology as the problem of inducing a description (of some …

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 …

We explore the use of morph-based language models in large-vocabulary continuous-
speech recognition systems across four so-called morphologically rich languages: Finnish …

Segmentation of clitics has been shown to improve accuracy on a variety of Arabic NLP
tasks. However, state-of-the-art Arabic word segmenters are either limited to formal Modern …

This paper summarizes our recent efforts for building a Turkish Broadcast News transcription
and retrieval system. The agglutinative nature of Turkish leads to a high number of out-of …

Speech recognition systems trained for morphologically rich languages face the problem of
vocabulary growth caused by prefixes, suffixes, inflections, and compound words. Solutions …

Because in agglutinative languages the number of observed word forms is very high,
subword units are often utilized in speech recognition. However, the proper use of subword …

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 …

Speech is the most natural way of human communication and in order to achieve convenient
and efficient human–computer interaction implementation of state-of-the-art spoken …