Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges

• URL: http://arxiv.org/abs/2410.11865v1
• Date: Mon, 07 Oct 2024 20:14:37 GMT
• Title: Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges
• Authors: Dancheng Liu, Jason Yang, Ishan Albrecht-Buehler, Helen Qin, Sophie Li, Yuting Hu, Amir Nassereldine, Jinjun Xiong,
• Abstract summary: Speech and language assessments (SLA) have been conducted by skilled speech-language pathologists (SLPs) There is a growing need for efficient and scalable SLA methods powered by artificial intelligence.
• Score: 15.727507607538874
• License:
• Abstract: 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.

Related papers

• Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition [110.8431434620642]
  We introduce the generative speech transcription error correction (GenSEC) challenge. This challenge comprises three post-ASR language modeling tasks: (i) post-ASR transcription correction, (ii) speaker tagging, and (iii) emotion recognition. We discuss insights from baseline evaluations, as well as lessons learned for designing future evaluations.
  arXiv  Detail & Related papers  (2024-09-15T16:32:49Z)
• Identification of Cognitive Decline from Spoken Language through Feature Selection and the Bag of Acoustic Words Model [0.0]
  The early identification of symptoms of memory disorders plays a significant role in ensuring the well-being of populations. The lack of standardized speech tests in clinical settings has led to a growing emphasis on developing automatic machine learning techniques for analyzing naturally spoken language. The work presents an approach related to feature selection, allowing for the automatic selection of the essential features required for diagnosis from the Geneva minimalistic acoustic parameter set and relative speech pauses.
  arXiv  Detail & Related papers  (2024-02-02T17:06:03Z)
• Affect Recognition in Conversations Using Large Language Models [9.689990547610664]
  Affect recognition plays a pivotal role in human communication. This study investigates the capacity of large language models (LLMs) to recognise human affect in conversations.
  arXiv  Detail & Related papers  (2023-09-22T14:11:23Z)
• SpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts [108.04306136086807]
  We present research that explores the application of prompt tuning to stimulate speech LMs for various generation tasks, within a unified framework called SpeechGen. The proposed unified framework holds great promise for efficiency and effectiveness, particularly with the imminent arrival of advanced speech LMs.
  arXiv  Detail & Related papers  (2023-06-03T22:35:27Z)
• BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models [56.93604813379634]
  Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels. We propose a language-acquisition-friendly benchmark to probe spoken language models at the lexical and syntactic levels. We highlight two exciting challenges that need to be addressed for further progress: bridging the gap between text and speech and between clean speech and in-the-wild speech.
  arXiv  Detail & Related papers  (2023-06-02T12:54:38Z)
• Understanding Spoken Language Development of Children with ASD Using Pre-trained Speech Embeddings [26.703275678213135]
  Natural Language Sample (NLS) analysis has gained attention as a promising complement to traditional methods. This paper proposes applications of speech processing technologies in support of automated assessment of children's spoken language development.
  arXiv  Detail & Related papers  (2023-05-23T14:39:49Z)
• Automatic Speech recognition for Speech Assessment of Preschool Children [4.554894288663752]
  The acoustic and linguistic features of preschool speech are investigated in this study. Wav2Vec 2.0 is a paradigm that could be used to build a robust end-to-end speech recognition system.
  arXiv  Detail & Related papers  (2022-03-24T07:15:24Z)
• Recent Progress in the CUHK Dysarthric Speech Recognition System [66.69024814159447]
  Disordered speech presents a wide spectrum of challenges to current data intensive deep neural networks (DNNs) based automatic speech recognition technologies. This paper presents recent research efforts at the Chinese University of Hong Kong to improve the performance of disordered speech recognition systems.
  arXiv  Detail & Related papers  (2022-01-15T13:02:40Z)
• An Attribute-Aligned Strategy for Learning Speech Representation [57.891727280493015]
  We propose an attribute-aligned learning strategy to derive speech representation that can flexibly address these issues by attribute-selection mechanism. Specifically, we propose a layered-representation variational autoencoder (LR-VAE), which factorizes speech representation into attribute-sensitive nodes. Our proposed method achieves competitive performances on identity-free SER and a better performance on emotionless SV.
  arXiv  Detail & Related papers  (2021-06-05T06:19:14Z)
• A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia among British Sign Language Users [5.8720142291102135]
  A timely diagnosis helps in obtaining necessary support and appropriate medication. Deep learning based approaches for image and video analysis and understanding are promising. We show that our approach is not over-fitted and has the potential to scale up.
  arXiv  Detail & Related papers  (2020-10-01T16:35:48Z)
• Silent Speech Interfaces for Speech Restoration: A Review [59.68902463890532]
  Silent speech interface (SSI) research aims to provide alternative and augmentative communication methods for persons with severe speech disorders. SSIs rely on non-acoustic biosignals generated by the human body during speech production to enable communication. Most present-day SSIs have only been validated in laboratory settings for healthy users.
  arXiv  Detail & Related papers  (2020-09-04T11:05:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.