# Bringing NURC/SP to Digital Life: the Role of Open-source Automatic Speech Recognition Models

arXiv: https://arxiv.org/abs/2210.07852 · HTML: https://mlsanity.com/paper/2210.07852/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [The Multilingual TEDx Corpus for Speech Recognition and Translation](https://mlsanity.com/paper/2102.01757/) (2021)
- [CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus](https://mlsanity.com/paper/2002.01320/) (2020)
- [Brazilian Portuguese Speech Recognition Using Wav2vec 2.0](https://mlsanity.com/paper/2107.11414/) (2021)
- [CORAA: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian Portuguese](https://mlsanity.com/paper/2110.15731/) (2021)
- [Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 Challenge](https://mlsanity.com/paper/2207.14418/) (2022)

## Built on by (most cited first, 1 of 1)

- [Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person](https://mlsanity.com/paper/2305.14580/) (2023)

## Abstract (arXiv)

The NURC Project that started in 1969 to study the cultured linguistic urban norm spoken in five Brazilian capitals, was responsible for compiling a large corpus for each capital. The digitized NURC/SP comprises 375 inquiries in 334 hours of recordings taken in S~ao Paulo capital. Although 47 inquiries have transcripts, there was no alignment between the audio-transcription, and 328 inquiries were not transcribed. This article presents an evaluation and error analysis of three automatic speech recognition models trained with spontaneous speech in Portuguese and one model trained with prepared speech. The evaluation allowed us to choose the best model, using WER and CER metrics, in a manually aligned sample of NURC/SP, to automatically transcribe 284 hours.