Mozilla Common Voice is a crowdsourced project that aims to create a public, multilingual dataset of voice recordings for training speech recognition models. In Common Voice, anyone can contribute by donating or validating recordings in various languages. However, despite the availability of many recordings in certain languages, a significant percentage remains unvalidated by users. This is the case for Spanish, where in version 17.0 of Common Voice, 75% of the 2,220 hours of recordings are unvalidated. In this work, we used the Whisper recognizer to automatically validate approximately 784 hours of recordings which are more than the 562 hours validated by users. To verify the accuracy of the validation, we developed a speech recognition model based on a version of NVIDIA-NeMo’s Parakeet, which does not have an official Spanish version. Our final model achieved a WER of less than 4% on the test and validation splits of Common Voice 17.0. Both the model and the speech corpus are publicly available on Hugging Face.

The platform samromur.is, or “Samrómur” for short, is a crowdsourcing web application built on Mozilla’s Common Voice, designed to accumulate speech data for the advancement of language technologies in Icelandic. Over the years, Samrómur has proven to be remarkably successful in amassing a significant number of high-quality audio clips from thousands of users. However, the challenge of manually verifying the entirety of the collected data has hindered its effective exploitation, especially in the realm of Automatic Speech Recognition (ASR), its original purpose. In this paper, we introduce the “Samrómur Milljón” corpus, an ASR dataset comprising one million audio clips from Samrómur. These clips have been automatically verified using state-of-the-art speech recognition systems such as NeMo, Wav2Vec2, and Whisper. Additionally, we present the ASR results obtained from creating acoustic models based on Samrómur Milljón. These results demonstrate significant promise when compared to other acoustic models trained with a similar volume of Icelandic data from different sources.

Samrómur Children is an Icelandic speech corpus intended for the field of automatic speech recognition. It contains 131 hours of read speech from Icelandic children aged between 4 to 17 years. The test portion was meticulously selected to cover a wide range of ages as possible; we aimed to have exactly the same amount of data per age range. The speech was collected with the crowd-sourcing platform Samrómur.is, which is inspired on the “Mozilla’s Common Voice Project”. The corpus was developed within the framework of the “Language Technology Programme for Icelandic 2019 − 2023”; the goal of the project is to make Icelandic available in language-technology applications. Samrómur Children is the first corpus in Icelandic with children’s voices for public use under a Creative Commons license. Additionally, we present baseline experiments and results using Kaldi.

This work presents the path toward the creation of eight Spoken Language Resources under the umbrella of the Mexican Social Service national program. This program asks undergraduate students to donate time and work for the benefit of their society as a requirement to receive their degree. The program has thousands of options for the students who enroll. We show how we created a program which has resulted in the creation of open language resources which now are freely available in different repositories. We estimate that this exercise is equivalent to a budget of more than half a million US dollars. However, since the program is based on retribution from the students to their communities there has not been a necessity of a financial budget.

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.