Speech dialects refer to linguistic and pronunciation variations in the speech of the same
language. Automatic dialect classification requires considerable acoustic and linguistic …

In this paper, we investigate different approaches for Dialect Identification (DID) in Arabic
broadcast speech. Dialects differ in their inventory of phonological segments. This paper …

This paper presents a dialect identification (DID) system based on the transformer neural
network architecture. The conventional convolutional neural network (CNN)-based systems …

This paper describes an Arabic Automatic Speech Recognition system developed on 15
hours of Multi-Genre Broadcast (MGB-3) data from YouTube, plus 1,200 hours of Multi …

In this paper, we investigate a set of methods for textual Arabic Dialect Identification, where
we considered word-level and sentence-level approaches. We used three classifiers …

We present the Factorial Deep Markov Model (FDMM) for representation learning of speech.
The FDMM learns disentangled, interpretable and lower dimensional latent representations …

As dialects are widely used in many countries, there is growing interest in incorporating
them into various applications, including conversational systems. Processing spoken …

In this paper, we explore the use of a factorized hierarchical variational autoencoder
(FHVAE) model to learn an unsupervised latent representation for dialect identification …

This work explores the application of various supervised classification approaches using
prosodic information for the identification of spoken North S\'ami language varieties. Dialects …

Speaker and language recognition and characterization is an exciting area of research that
has gained importance in the field of speech science and technology. This special issue …