A5038284453- Phonetics and Phonology Research
- Voice and Speech Disorders
- Speech Recognition and Synthesis
- Nutritional Studies and Diet
- Speech and dialogue systems
- Nutrition and Health in Aging
- Neurobiology of Language and Bilingualism
- Music and Audio Processing
- Language Development and Disorders
- Urinary and Genital Oncology Studies
- Gastrointestinal disorders and treatments
- Urological Disorders and Treatments
- Natural Language Processing Techniques
This study presents a model of automatic speech recognition (ASR) designed to diagnose pronunciation issues in children with sound disorders (SSDs) replace manual transcriptions clinical procedures. Since ASR models trained for general purposes primarily predict input into real words, employing well-known high-performance evaluating SSDs is impractical. We fine-tuned the wav2vec 2.0 XLS-R recognize as pronounced rather than existing words. The was dataset from 137 inadequate production...
This study presents a model of automatic speech recognition (ASR) that is designed to diagnose pronunciation issues in children with sound disorders (SSDs) replace manual transcriptions clinical procedures. Because ASR models trained for general purposes mainly predict input into standard spelling words, well-known high-performance are not suitable evaluating SSDs. We fine-tuned the wav2vec2.0 XLS-R recognise words as they pronounced by children, rather than converting their words. The was...
Speech sound disorders (SSDs) are common communication challenges in children, typically assessed by speech-language pathologists (SLPs) using standardized tools. However, traditional evaluation methods time-intensive and prone to variability, raising concerns about reliability.
<sec> <title>BACKGROUND</title> Speech sound disorders (SSDs) are common communication challenges in children, typically assessed by speech-language pathologists (SLPs) using standardized tools. However, traditional evaluation methods time-intensive and prone to variability, raising concerns about reliability. </sec> <title>OBJECTIVE</title> This study aimed compare the outcomes of SLPs an automatic speech recognition (ASR) model two SSD assessments South Korea, evaluating ASR model’s...
Abstract This study examines the effects of asymmetrical mappings L2 sounds to L1 on real-time processing phonology. L1-Korean participants completed a self-paced listening (SPL) task paired with picture verification (PV) task, in which an English sentence was presented word by along that matched or mismatched sentence. In critical region, vowel deliberately replaced wrong for two types pairs: Type 1: pairs showing one-to-one mapping Korean counterparts (e.g., English: /i/ and /æ/ /æ/,...
Speech inversion (acoustic-to-articulatory mapping) is not a trivial problem, despite the importance, due to highly non-linear and non-unique nature. This study aimed investigate performance of Deep Neural Network (DNN) compared that traditional Artificial (ANN) address problem. The Wisconsin X-ray Microbeam Database was employed acoustic signal articulatory pellet information were input output in models. Results showed ANN deteriorated as number hidden layers increased. In contrast, DNN...