ASR Language Resources for F aroese (2023)

Abstract: The aim of this work is to present a set of novel language resources in Faroese suitable for the field of Automatic Speech Recognition including: an ASR corpus comprised of 109 hours of transcribed speech data, acoustic models in systems such as WAV2VEC2, NVIDIA-NeMo, Kaldi and PocketSphinx; a set of n-gram language models and a set of pronunciation dictionaries with two different variants of Faroese. We also show comparison results between the distinct acoustic models presented here. All the resources exposed in this document are publicly available under creative commons licences.

## introduction as the digital world has become increasingly prominent and omnipresent in most human activities, the use of more and better language technologies has become a pressing need. for this reason, more and more governments are investing in the development of all kinds of linguistic resources that allow their citizens to be part of the new digital era, with all the benefits it entails. language technology initiatives in the main regions of the world such as: europe @xcite @xcite , india @xcite , africa @xcite , china @xcite , saudi arabia @xcite and the spanish speaking countries @xcite ; allow us to attest how important language technologies have become in recent times. in synchrony with all the developments mentioned above, it is time to talk about the efforts made for the development of the faroese language in the digital sphere. the most recent initiative in this regard is the ravnur project, founded in the faroe islands. thanks to the resources generated and shared by ravnur, it has been possible to develop all the language resources presented in this document. ## the ravnursson corpus ravnursson foot_0 (hernández mena and @xcite is an asr corpus with a length of 109 hours foot_1 , extracted from the blark described in section 1.3. unlike the original blark, the ravnursson only contains the speech files along with their respective transcriptions. the main characteristics of the corpus are the following: ## acoustic models the development of the ravnursson corpus allowed us to create acoustic models in four different asr systems: wav2vec2, nemo, kaldi and pocketsphinx. in this section we discuss the details of how we created each of them. ## pronunciation models the pronunciation models that we discuss in this section is a set of pronouncing dictionaries that are included in our "faroese language models with pronunciations" @xcite along with a number of language models that will be discussed in section 5. most of the pronunciations come from the original blark, but for convenience, we subdivide them in different dictionaries as follows: ## language models as it was mentioned in section 4, our "faroese language models with pronunciations" is a set of n-gram language models of distinct sizes that were created using the faroese text provided in the blark, as it provides with text from newspaper articles, parliamentary speeches, books and more. the normalization process of that text included to change everything to lowercase, allow only characters belonging to the faroese alphabet and removing punctuation marks. the resulting text has a length of more than half million lines of text (106.3mb approximately). the text was used to create a 3-gram (recommended for decoding) and a 4-gram (recommended for re-scoring) language models with the srilm toolkit @xcite . both the 3-gram and 4-gram models come in pruned and unpruned versions. it also includes a 6-gram language model in binary format suitable for asr experiments with the nemo toolkit. in particular, this model was created using kenlm @xcite . it is important to mention that all the words present in any of the language models are present in the pronouncing dictionaries for the east and central variants of faroese (see section 4). ## results the nemo results include the wer obtained using the 6-gram language model (lm) presented in section 5 as well as the wer obtained with no language model at all. the kaldi results include the wer obtained with hidden markov models (hmm) only and the wer obtained with the lstm network. as it can be seen, the best results are obtained with the wav2vec2 model, which is not a surprise as it is well known that it can achieve acceptable results with less than 1 hour of speech data. what is remarkable indeed, is the gap of performance between wav2vec2 and the other systems. in addition to this, based on our previous experience @xcite , it is also remarkable that the wer obtained with nemo using a language model and the wer obtained with kaldi using the lstm are so close to each other despite of the relatively low amount of training data. this fact reveals that the train- on the other hand, ## conclusions a major development of faroese asr is presented in this work. the ravnursson project has produced a corpus of 109 hours of transcribed speech and acoustic models for wav2vec2, nemo, kaldi and pocketsphinx have been developed. furthermore, the project has also produced a set of n-gram language models of distinct sizes and pronunciation dictionaries in faroese suitable for asr experimentation. quality assessment of the acoustic models are shown in