Results 201 to 210 of about 227,499 (372)
How we should measure orthographic depth: Or should we? [PDF]
Psychon Bull RevSchmalz X, Rueckl JG, Siegelman N.
europepmc +1 more source
Nigerian English: History, functions and features
World Englishes, Volume 45, Issue 1, Page 7-18, March 2026.Abstract This article offers a comprehensive overview of Nigerian English, a rapidly expanding variety of world Englishes, recognised as one of the fastest‐growing varieties of English globally in numerical terms. This article has four aims. First, it discusses the historical developments of English in Nigeria with reference to the events that led to ...
Kingsley O. Ugwuanyi +1 more
wiley +1 more source
Zeuxis y azeuxis : más sobre vocales en contacto = Zeuxis and azeuxis : more on vowels in contact
, 2013 Esteban Torre
openalex +1 more source
Acoustic Vowel Metrics as Correlates of Dysphagia and Dysarthria in Brainstem Neurodegenerative Diseases. [PDF]
Audiol ResCapobianco S +7 more
europepmc +1 more source
Sociolinguistic variation in the rhythm of Nigerian English speech
World Englishes, Volume 45, Issue 1, Page 125-143, March 2026.Abstract There is a dearth of research on speech rhythm in Nigerian English, and scholars have differing views on its nature, ranging from being syllable‐timed to tone‐timed. Apart from the fact that the majority of few available studies were conducted more than a decade ago, the exact nature of speech rhythm in Nigerian English remains contentious ...
Folajimi Oyebola, Osemudiamhe Ilekura
wiley +1 more source
Speech development in infants—vowel production [PDF]
, 1976 Philip Lieberman +4 more
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Do 'leaders' in change sound different from 'laggers'? The perceptual similarity of New Zealand English voices. [PDF]
PLoS OneSheard E +3 more
europepmc +1 more source
Use of Automation Technologies and Data Mining in Speech Recognition for Autism
Brain and Behavior, Volume 16, Issue 2, February 2026.Pipeline 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.
Rongjie Mao, Yuncheng Zhu
wiley +1 more source