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 …
W Lin, M Madhavi, RK Das, H Li - … International Conference on …, 2020 - ieeexplore.ieee.org
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 …
S Shon, WN Hsu, J Glass - 2018 IEEE Spoken Language …, 2018 - ieeexplore.ieee.org
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 …