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.

Corpus de Investigación en Español de México del Posgrado de Ingeniería Eléctrica y Servicio Social” (CIEMPIESS) is a new open-sourced corpus extracted from Spanish spoken FM podcasts in the dialect of the center of Mexico. The CIEMPIESS corpus was designed to be used in the field of automatic speech recongnition (ASR) and it is provided with two different kind of pronouncing dictionaries, one of them containing the phonemes of Mexican Spanish and the other containing this same phonemes plus allophones. Corpus annotation took into account the tonic vowel of every word and the four different sounds that letter “x” presents in the Spanish language. CIEMPIESS corpus is also provided with two different language models extracted from electronic newsletters, one of them takes into account the tonic vowels but not the other one. Both the dictionaries and the language models allow users to experiment different scenarios for the recognition task in order to adequate the corpus to their needs.