Sirvi Autor "Salvi, Giampiero" järgi

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A character-based analysis of impacts of dialects on end-to-end Norwegian ASR
(University of Tartu Library, 2023-05) Parsons, Phoebe; Kvale, Knut; Svendsen, Torbjørn; Salvi, Giampiero
Adding Metadata to Existing Parliamentary Speech Corpus
(University of Tartu Library, 2025-03) Parsons, Phoebe; Solberg, Per Erik; Kvale, Knut; Svendsen, Torbjørn; Salvi, Giampiero; Johansson, Richard; Stymne, Sara
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.
Improving Generalization of Norwegian ASR with Limited Linguistic Resources
(University of Tartu Library, 2023-05) Solberg, Per Erik; Ortiz, Pablo; Parsons, Phoebe; Svendsen, Torbjørn; Salvi, Giampiero
Match ‘em: Multi-Tiered Alignment for Error Analysis in ASR
(University of Tartu Library, 2025-03) Parsons, Phoebe; Kvale, Knut; Svendsen, Torbjørn; Salvi, Giampiero; Johansson, Richard; Stymne, Sara
We introduce “Match ‘em”: a new framework for aligning output from automatic speech recognition (ASR) with reference transcriptions. This allows a more detailed analysis of errors produced by end-to-end ASR systems compared to word error rate (WER). Match ‘em performs the alignment on both the word and character level; each relying on information from the other to provide the most meaningful global alignment. At the character level, we define a speech production motivated character similarity metric. At the word level, we rely on character similarities to define word similarity and, additionally, we reconcile compounding (insertion or deletion of spaces). We evaluated Match ‘em on transcripts of three European languages produced by wav2vec2 and Whisper. We show that Match ‘em results in more similar word substitution pairs and that compound reconciling can capture a broad range of spacing errors. We believe Match ‘em to be a valuable tool for ASR error analysis across many languages.