With large amounts of training data, it is possible to train ASR models that generalize well across speakers and domains. But how do you train robust models when there is a limited amount of available training data? In the experiments reported here, we fine-tuned a pre-trained wav2vec2 ASR model on two transcribed, Norwegian speech datasets, one with parliamentary speech and one with radio recordings, as well as on combinations of the two datasets. We subsequently tested these models on different test sets with planned and unplanned speech and with speakers of various dialects. Our results show that models trained on combinations of the two datasets generalize better to new data than the single-dataset models, even when the length of the training data is the same. Our lexical analysis sheds light on the type of mistakes made by the models and on the importance of consistent standardization when training combined models of this kind.

With the advent of weakly supervised ASR systems like Whisper, it is possible to train ASR systems on non-verbatim transcriptions. This paper describes an effort to create a large Norwegian dataset for weakly supervised ASR from parliamentary recordings. Audio from Stortinget, the Norwegian parliament, is segmented and transcribed with an existing ASR system. An algorithm retrieves transcripts of these segments from Stortinget’s official proceedings using the Levenshtein edit distance between the ASR output and the proceedings text. In that way, a dataset of more than 5000 hours of transcribed speech is produced with limited human effort. Since parliamentary data is public domain, the dataset can be shared freely without any restrictions.

The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with recordings of meetings from Stortinget, the Norwegian parliament. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition (ASR) systems. The recordings are manually transcribed and annotated with language codes and speakers, and there are detailed metadata about the speakers. The transcriptions exist in both normalized and non-normalized form, and non-standardized words are explicitly marked and annotated with standardized equivalents. To test the usefulness of this dataset, we have compared an ASR system trained on the NPSC with a baseline system trained on only manuscript-read speech. These systems were tested on an independent dataset containing spontaneous, dialectal speech. The NPSC-trained system performed significantly better, with a 22.9% relative improvement in word error rate (WER). Moreover, training on the NPSC is shown to have a “democratizing” effects in terms of dialects, as improvements are generally larger for dialects with higher WER from the baseline system.

This paper presents the NDC Treebank of spoken Norwegian dialects in the Bokmål variety of Norwegian. It consists of dialect recordings made between 2006 and 2012 which have been digitised, segmented, transcribed and subsequently annotated with morphological and syntactic analysis. The nature of the spoken data gives rise to various challenges both in segmentation and annotation. We follow earlier efforts for Norwegian, in particular the LIA Treebank of spoken dialects transcribed in the Nynorsk variety of Norwegian, in the annotation principles to ensure interusability of the resources. We have developed a spoken language parser on the basis of the annotated material and report on its accuracy both on a test set across the dialects and by holding out single dialects.

We present the Norwegian Anaphora Resolution Corpus (NARC), the first publicly available corpus annotated with anaphoric relations between noun phrases for Norwegian. The paper describes the annotated data for 326 documents in Norwegian Bokmål, together with inter-annotator agreement and discussions of relevant statistics. We also present preliminary modelling results which are comparable to existing corpora for other languages, and discuss relevant problems in relation to both modelling and the annotations themselves.

Although treebanks annotated according to the guidelines of Universal Dependencies (UD) now exist for many languages, the goal of annotating the same phenomena in a cross-linguistically consistent fashion is not always met. In this paper, we investigate one phenomenon where we believe such consistency is lacking, namely expletive elements. Such elements occupy a position that is structurally associated with a core argument (or sometimes an oblique dependent), yet are non-referential and semantically void. Many UD treebanks identify at least some elements as expletive, but the range of phenomena differs between treebanks, even for closely related languages, and sometimes even for different treebanks for the same language. In this paper, we present criteria for identifying expletives that are applicable across languages and compatible with the goals of UD, give an overview of expletives as found in current UD treebanks, and present recommendations for the annotation of expletives so that more consistent annotation can be achieved in future releases.

The Norwegian Dependency Treebank is a new syntactic treebank for Norwegian Bokmäl and Nynorsk with manual syntactic and morphological annotation, developed at the National Library of Norway in collaboration with the University of Oslo. It is the first publically available treebank for Norwegian. This paper presents the core principles behind the syntactic annotation and how these principles were employed in certain specific cases. We then present the selection of texts and distribution between genres, as well as the annotation process and an evaluation of the inter-annotator agreement. Finally, we present the first results of data-driven dependency parsing of Norwegian, contrasting four state-of-the-art dependency parsers trained on the treebank. The consistency and the parsability of this treebank is shown to be comparable to other large treebank initiatives.