A Review: Devnagri Speech to Text for Marathwada Region

Modale, Snehal; Sable, Ganesh S.; Deshmukh, Ratnadeep R.

doi:10.1007/978-981-15-1002-1_53

Cited by 3 publications

(1 citation statement)

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“…If the language the user speaks differs from what the Speech-to-Text application expects, the transcription will likely be less accurate. For example, if the system is set to transcribe English, but the user speaks Arabic, the output will likely be flawed [88].…”

Section: Discussionmentioning

confidence: 99%

The Detection of Dysarthria Severity Levels Using AI Models: A Review

Al-Ali,

Al-Maadeed,

Saleh

et al. 2024

IEEE Access

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Dysarthria, a speech disorder stemming from neurological conditions, affects communication and life quality. Precise classification and severity assessment are pivotal for therapy but are often subjective in traditional speech-language pathologist evaluations. Machine learning models offer objective assessment potential, enhancing diagnostic precision. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels, highlighting effective features for automatic classification and optimal AI techniques. We systematically reviewed the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.

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Section: Discussionmentioning

confidence: 99%