Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917804445270016 |
|---|---|
| author | Liu, Dancheng Yang, Jason Albrecht-Buehler, Ishan Qin, Helen Li, Sophie Hu, Yuting Nassereldine, Amir Xiong, Jinjun |
| author_facet | Liu, Dancheng Yang, Jason Albrecht-Buehler, Ishan Qin, Helen Li, Sophie Hu, Yuting Nassereldine, Amir Xiong, Jinjun |
| contents | Speech is a fundamental aspect of human life, crucial not only for communication but also for cognitive, social, and academic development. Children with speech disorders (SD) face significant challenges that, if unaddressed, can result in lasting negative impacts. Traditionally, speech and language assessments (SLA) have been conducted by skilled speech-language pathologists (SLPs), but there is a growing need for efficient and scalable SLA methods powered by artificial intelligence. This position paper presents a survey of existing techniques suitable for automating SLA pipelines, with an emphasis on adapting automatic speech recognition (ASR) models for children's speech, an overview of current SLAs and their automated counterparts to demonstrate the feasibility of AI-enhanced SLA pipelines, and a discussion of practical considerations, including accessibility and privacy concerns, associated with the deployment of AI-powered SLAs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11865 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges Liu, Dancheng Yang, Jason Albrecht-Buehler, Ishan Qin, Helen Li, Sophie Hu, Yuting Nassereldine, Amir Xiong, Jinjun Audio and Speech Processing Computation and Language Quantitative Methods Speech is a fundamental aspect of human life, crucial not only for communication but also for cognitive, social, and academic development. Children with speech disorders (SD) face significant challenges that, if unaddressed, can result in lasting negative impacts. Traditionally, speech and language assessments (SLA) have been conducted by skilled speech-language pathologists (SLPs), but there is a growing need for efficient and scalable SLA methods powered by artificial intelligence. This position paper presents a survey of existing techniques suitable for automating SLA pipelines, with an emphasis on adapting automatic speech recognition (ASR) models for children's speech, an overview of current SLAs and their automated counterparts to demonstrate the feasibility of AI-enhanced SLA pipelines, and a discussion of practical considerations, including accessibility and privacy concerns, associated with the deployment of AI-powered SLAs. |
| title | Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges |
| topic | Audio and Speech Processing Computation and Language Quantitative Methods |
| url | https://arxiv.org/abs/2410.11865 |