Adding Metadata to Existing Parliamentary Speech Corpus (2025)

Abstract: Parliamentary proceedings are convenient data sources for creating corpora for speech technology. Given its public nature, there is an abundance of extra information about the speakers that can be legally and ethically harvested to enrich this kind of corpora. This paper describes the methods we have used to add speaker metadata to the Stortinget Speech Corpus (SSC) containing over 5,000 hours of Norwegian speech with non-verbatim transcripts but without speaker metadata. The additional metadata for each speech segment includes speaker ID, gender, date of birth, municipality of birth, and counties represented. We also infer speaker dialect from their municipality of birth using a manually designed mapping between municipalities and Norwegian dialects. We provide observations on the SSC data and give suggestions for how it may be used for tasks other than speech recognition. Finally, we demonstrate the utility of this new metadata through a dialect identification task. The described methods can be adapted to add metadata information to parliamentary corpora in other languages.

## introduction there has been, historically, a lack of high quality, freely available speech resources for machine learning tasks. traditionally, these resources have been created to facilitate development of automatic speech recognition (asr) models, and as such have been expensive to create, requiring human hours for both data collection and then careful, verbatim transcription. even for "well resourced" languages like english, datasets rarely exceeded 1,000 hours. however, as new asr technologies loosen the requirements for transcription precision, this allows for even larger datasets that are created from less verbatim sources @xcite . these new, more loosely supervised datasets often lack details found in older, more traditional speech resources and are therefore potentially limited in their application. many established speech resources are composed of relatively short duration segments with speech from only one speaker at a time. additionally, this speaker is often known (even if only by an anonymized speaker identifier) and metadata, such as age and gender, is given about them. this richness of metadata allows for speech technology and machine learning tasks beyond asr -such as language (or dialect) identification, speaker diarization, speaker identification or verification. crucial to all these tasks is knowledge about who is speaking. recently, a number of speech corpora were created from public domain recordings of parliamentary proceedings; for instance, iceland @xcite , denmark @xcite , finland @xcite , croatia @xcite and the european parliament @xcite . in all of these works it is known, at the very least, who is speaking in each segment (either by name or speaker id), with most also including gender information. @xcite their dataset using the rich metadata they were able to pull from an open api providing both distribution information and asr results along age, gender, and educational background lines. however, it appears that this rich metadata was not released with the final dataset. ljubešic et al. included name, gender, year of birth, party affiliation and party status for their speakers. in 2023, the national library of norway (nb) developed the stortinget speech corpus (ssc) @xcite using data from the norwe-gian parliament (called stortinget in norwegian). in early 2024, nb published the results of their analysis of several asr systems for norwegian @xcite . in this report they showed that whisper models @xcite , finetuned on the ssc and some additional smaller datasets, performed best on an unseen test set created from radio and tv program audio. this finetuned model outperformed both the base whisper model as well as fine-tuned wav2vec @xcite ) models and commercial asr systems from google and microsoft, thus demonstrating the importance of this speech corpus in combination with the whisper architecture for asr. despite the ssc's obvious utility in training well-performing asr models, it, as originally created, contains no metadata for each speech segment. we believe the effort to construct the missing metadata has merit as expanding the ssc into other speech technology domains would be a benefit for norwegian speech research. to that end, we have undertaken the effort of ensuring that each segment in the ssc has been matched to a speaker identifier and that public speaker metadata has been added. as a result of this effort, this new metadata is now included with the ssc and made available by the norwegian language bank at the national library of norway. furthermore, we offer a recommendation for a subset of the ssc that more closely resembles traditional well annotated speech corpora and may be more applicable to other speech tasks. finally, we believe that the efforts described in this paper can be easily extended and applied to similar corpora in other languages and countries. ## the pre-existing ssc dataset the ssc contains more than 5,000 hours of natural norwegian speech paired with non-verbatim transcripts created from the norwegian parliament. the national library of norway created the ssc by following the technique described by @xcite . they first broke the plenary meetings into segments using voice activity detection. shorter segments were combined resulting in each ssc segment being roughly 30 seconds. in doing this, no concern was given to speaker boundaries. that is, the 30 second segments were created from files containing recordings of a whole day's worth of parliamentary discussion, without awareness of who was speaking or whether there was one or multiple speakers in the segment. thus for each segment in the ssc, no speaker metadata is available. after the audio had been segmented, an asr system was then used to generate transcripts for these segments. the levenshtein ratio foot_0 was then used to align the asr output with the text of the official parliamentary proceedings sourced from the parlamint-no corpus foot_1 . the proceedings were human-transcribed at the utterance level with some light editing and omissions for standardization and legibility. because the official proceedings are not a verbatim transcription of the spoken utterances, the asr transcriptions may deviate considerably from the proceedings. consequently, only segments where the score produced by the levenshtein ratio between the proceedings text and the asr text was above a threshold (0.5) were kept. for the selected 30 second segments, the proceedings text was taken as the transcription. the levenshtein ratio score was also kept in the ssc. in this manner, the ssc was created. ## data observations as stated earlier, the ssc was designed for loosely supervised asr training, and has already been used for this aim foot_5 . however, other speech tasks require either a greater degree of faithfulness in transcription or audio with only one speaker per segment, or both. in order to understand which, if any, part of the ssc might be useful in these other tasks, an analysis of the data was performed. ## comparison with the npsc the norwegian parliamentary speech corpus (npsc) (solberg and ortiz, 2022) was created using data from 41 days of norwegian parliament recordings where humans manually segmented and transcribed the data. thus, the npsc composes a small subset of the data available in the ssc. after listening to each speaker, the transcribers assigned each speaker in the npsc a dialect. five dialect regions were used for this task: eastern norway (from here on called east), western norway (west), northern norway (north), trøndelag (mid), and southern norway (south). given this careful human supervision, the npsc utterances may then serve as a "ground truth" for verbatim text, as well as speaker identities and dialects. to reconcile the npsc and the ssc, we could not use word offsets as the words are from fundamentally different sources (verbatim transcription versus official proceedings). however, the millisecond offset from the beginning of the day's recording was preserved in both the npsc and ssc segments. therefore, we were able to use these millisecond offsets and the same approach as described with the word offsets to determine which npsc utterances corresponded to each ssc segment. ## automatic dialect identification to demonstrate the utility of these new dialect labels, we have investigated the task of automatic dialect classification. ## conclusion through the efforts described in the paper, we enrich the ssc with speaker id, gender, written form, age, dialect, municipality and county of birth and counties represented for each ssc segment. although the methods are developed for the norwegian parliament, we believe they can relatively easily be adapted to parliamentary speech corpora in other languages. the further aim of our work herein was to provide a subset of the large ssc that could be used for tasks beyond asr. thus, we provided observations on the corpus and suggested suitable subsets for different tasks in speech technology. we demonstrated the utility of this new metadata through a dialect identification task. the model trained using ssc outperformed the model trained with a smaller parliamentary corpus, thus showing an benefit of a corpus of the ssc's size. finally, as a continuation of @xcite and @xcite , this work provides a general template for how public datasets, such as parliamentary recordings, may be transformed into corpora for machine learning.