Morphological analysis for the Maltese language : the challenges of a hybrid system

Maltese is a morphologically rich language with a hybrid morphological system which features both concatenative and non-concatenative processes. This paper analyses the impact of this hybridity on the performance of machine learning techniques for morphological labelling and clustering. In particular, we analyse a dataset of morphologically related word clusters to evaluate the difference in results for concatenative and non-concatenative clusters. We also describe research carried out in morphological labelling, with a particular focus on the verb category. Two evaluations were carried out, one using an unseen dataset, and another one using a gold standard dataset which was manually labelled. The gold standard dataset was split into concatenative and non-concatenative to analyse the difference in results between the two morphological systems.non peer-reviewe

Early ambulation and discharge after four French femoral artery catheterisation for diagnostic coronary angiography

One of the bottlenecks experienced by cardiac catheterisation laboratories is the amount of time that patients must remain on the unit after femoral artery puncture. This remains a contentious issue and there are a few studies to guide units with regard to how early it is possible to safely ambulate and discharge patients home.peer-reviewe

Hymn to the heroes of Malta

Ġabra ta’ poeżiji u proża li tinkludi: Alla fil-ħolqien ta’ Ġużè Agius Bonello – Is-sena u l-bniedem ta’ Ġużè Ellul-Mercer – Li tiżra’ taħsad ta’ Vic. Apap – Huwa ta’ Gino Muscat-Azzopardi – Żewġ friefet ta’ Vincent Caruana – Iċ-ċagħka ta’ Ġużè Borg – Warda midbiela ta’ C. Gauci – It-tfajla tas-sulfarini ta’ Albert M. Cassola – L-aħħar traduzzjoni ta’ May Butcher qabel ma mietet – Hymn to the heroes of Malta.N/

Crowd-sourcing evaluation of automatically acquired, morphologically related word groupings

The automatic discovery and clustering of morphologically related words is an important problem with several practical applications. This paper describes the evaluation of word clusters carried out through crowd-sourcing techniques for the Maltese language. The hybrid (Semitic-Romance) nature of Maltese morphology, together with the fact that no large-scale lexical resources are available for Maltese, make this an interesting and challenging problem.peer-reviewe

Analysis of Data Augmentation Methods for Low-Resource Maltese ASR

Recent years have seen an increased interest in the computational speech processing of Maltese, but resources remain sparse. In this paper, we consider data augmentation techniques for improving speech recognition for low-resource languages, focusing on Maltese as a test case. We consider three different types of data augmentation: unsupervised training, multilingual training and the use of synthesized speech as training data. The goal is to determine which of these techniques, or combination of them, is the most effective to improve speech recognition for languages where the starting point is a small corpus of approximately 7 hours of transcribed speech. Our results show that combining the data augmentation techniques studied here lead us to an absolute WER improvement of 15% without the use of a language model.Comment: 12 page

Lipids, lipoproteins and prevalence of familial hypercholesterolemia in the Faroe Islands – Results from a nationwide laboratory database

BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is one of the most common hereditary disorders. The population of the Faroe Islands was established by few founders, and genetic drift may have influenced lipid levels. The aim of this study was to describe the lipid distribution by providing age and sex-specific lipid values and to investigate the prevalence of FH in the Faroe Islands. METHODS: We used an electronic nationwide laboratory database that included lipid measurements obtained in the Faroe Islands between January 2006 and September 2020. Percentiles of lipid levels were calculated using quantile regression. The prevalence of FH was estimated according to the Make Early Diagnosis Prevent Early Death (MEDPED) diagnostic criteria and according to the LDL-C cut-off levels included in the Dutch Lipid Clinic Network (DLCN) criteria using generalized linear models with robust variance. RESULTS: According to the MEDPED age-specific cut-offs for LDL-C, a total of 216 subjects met the criteria for definite FH among 30,711 individuals corresponding to a prevalence of 0.70% (1:142). According to the LDL-C cut-offs included in the DLCN criteria, a total of 3,823 (1:8) subjects could be classified as having possible FH, and 10 (1:3,071) subjects could be classified as probable FH corresponding to a prevalence of 12.4% and 0.03%, respectively. Also, we found significant differences in lipid levels according to sex and age groups. CONCLUSION: The Faroe Islands might represent a founder population with a prevalence of possible FH as high as 1 in 8. Further investigation of genetic and clinical characteristics of FH in the Faroe Islands is needed

MASRI-HEADSET: A Maltese Corpus for Speech Recognition

Maltese, the national language of Malta, is spoken by approximately 500,000 people. Speech processing for Maltese is still in its early stages of development. In this paper, we present the first spoken Maltese corpus designed purposely for Automatic Speech Recognition (ASR). The MASRI-HEADSET corpus was developed by the MASRI project at the University of Malta. It consists of 8 hours of speech paired with text, recorded by using short text snippets in a laboratory environment. The speakers were recruited from different geographical locations all over the Maltese islands, and were roughly evenly distributed by gender. This paper also presents some initial results achieved in baseline experiments for Maltese ASR using Sphinx and Kaldi. The MASRI-HEADSET Corpus is publicly available for research/academic purposes.Comment: 8 pages, 2 figures, 4 tables, 1 appendix. Appears in Proceedings of the 12th edition of the Language Resources and Evaluation Conference (LREC'20