Results 11 to 20 of about 420 (159)
Analysis and categorization of the Rusyn language using the whisper model: demographic influences on linguistic convergence [PDF]
International Journal of Electronics and TelecommunicationsThe article presents a detailed linguistic analysis of the Rusyn language, focusing on its complex and evolving features, such as pronunciation, as well as individual, regional, and historical variabilities.
Paweł Małecki
doaj +3 more sources
CoRR, 2023 This article introduces Mi-Go, a novel testing framework aimed at evaluating the performance and adaptability of general-purpose speech recognition machine learning models across diverse real-world scenarios. The framework leverages YouTube as a rich and continuously updated data source, accounting for multiple languages, accents, dialects, speaking ...
Tomasz Wojnar +2 more
openaire +3 more sources
Transcription of Audio and Video with OpenAI’s Whisper
TCB: Technical Services in Religion & TheologyMargarete Wilkey, Geoffrey Wood
exaly +2 more sources
Evaluating OpenAI’s Whisper ASR: Performance Analysis Across Diverse Accents and Speaker Traits
JASA Express Letters, 2023 This research explores the performance of the Whisper's ASR system on different native and non-native English accents. The findings indicate better performance on North American vs British and Irish English accents; and on native vs native accents. The analysis also unearths links between speaker traits (sex, L1 typology, and L2 proficiency) and word ...
Graham, Calbert, Roll, Nathan
openaire +2 more sources
JMIR Mental Health, 2023 BackgroundAutomatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate
Salman Seyedi +11 more
doaj +1 more source
Retranscrire avec whisper via huma-num
, 2023 Cela fait maintenant plusieurs mois que le bruit court : un logiciel gratuit permettrait d'obtenir des retranscriptions automatiques d'une qualité excellente. Il s'agit de "Whisper", développé par OpenAI.
Aden Gaide
core +1 more source
BENCHMARKING WHISPER OPENAI ON SARAWAK LANGUAGES [PDF]
, 2023 The end-to-end (E2E) model is influentially reshaping the automatic speech recognition (ASR) scene, supplanting traditional ASR models such as the Hidden Markov model (HMM) and Deep Neural Network (DNN)-based hybrid models.
GERALD EINSTEIN CORNELIUS
core
CoRR, 2023 Automatic speech recognition (ASR) systems play a key role in applications involving human-machine interactions. Despite their importance, ASR models for the Portuguese language proposed in the last decade have limitations in relation to the correct identification of punctuation marks in automatic transcriptions, which hinder the use of transcriptions ...
Lucas Rafael Stefanel Gris +5 more
openaire +2 more sources
Use of Automation Technologies and Data Mining in Speech Recognition for Autism. [PDF]
Brain BehavPipeline analyzes clinical and naturalistic speech using LENA, wav2vec 2.0, and foundation‐model ASR (Whisper) to enable scalable ASD detection and severity estimation. Future work integrates benchmarking, privacy‐preserving collaboration (federated learning), and explainable, edge‐ready AI for clinically credible assessment and longitudinal monitoring.
Mao R, Zhu Y.
europepmc +2 more sources
Assessing the Reliability, Accuracy, and Relevance of Artificial Intelligence Speech Recognition for Clinical Documentation: A Scoping Review. [PDF]
J Eval Clin PractABSTRACT Background Background Clinical documentation is a major contributor to clinician workload and burnout, with physicians spending more than half of their workday on electronic health record (EHR) tasks. Artificial intelligence (AI)–based speech recognition (ASR) tools promise to reduce this burden by generating draft notes from dictated or ...
Atiku S, Owolanke K, Olakotan O.
europepmc +2 more sources