Uncertainty estimation for a speech recognition system
保存先:
| 著者: | , |
|---|---|
| フォーマット: | artículo original |
| 状態: | Versión publicada |
| 出版日付: | 2024 |
| その他の書誌記述: | Whisper is a voice recognition system designed by the company OpenAI, which has been trained with 680,000 hours of multilingual and multitask supervised data collected from the web. The following research aims to adapt and employ the Monte Carlo Dropout using audio data labeled in Spanish and contaminated with a certain amount of noise and Levensthein distance to estimate the score uncertainty of this system.Preliminary results show that there is a linear relationship between uncertainty estimation and the Word Error Rate (WER) of the transcriptions. Furthermore, it is observed that the number of insertions or omissions in the transcriptions tends to be low. |
| 国: | Portal de Revistas TEC |
| 機関: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| 言語: | Español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/7305 |
| オンライン･アクセス: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/7305 |
| キーワード: | Uncertainty Speech Recognition ASR Whisper Monte Carlo Dropout Incertidumbre Reconocimiento de voz |