自动语音识别的反,在健康的说话者中引起清晰的说话
Sarah E Gutz1,2, Marc F Maffei1, Jordan R Green1,2
1Department of Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, MA.
American journal of speech-language pathology
|October 12, 2023
概括
自动语音识别 (ASR) 反有效地促进健康个体的清晰语言,类似于传统的清晰语言方法. 这项技术对家庭语音练习和未来在失节症中的应用有前途.
科学领域:
- 语音语言病理学 语言病理学
- 生物医学工程 生物医学工程
- 声学语音学的声音学
背景情况:
- 清晰的言语对于理解能力至关重要,特别是对于患有发言障碍的人来说,如失联症.
- 获得清晰语言的传统方法往往需要临床医生的直接反.
- 开发可访问的家庭练习方法对于改善语音结果至关重要.
研究的目的:
- 评估自动语音识别 (ASR) 反在健康年轻人中引起清晰言语的有效性.
- 建立对ASR反对患有脱发性关节障碍的人的潜在影响的初步理解.
- 探索ASR作为一种工具,以促进独立的,在家里的清晰语言练习.
主要方法:
- 23名健康成年人在三种条件下产生言语:习惯性,清晰和ASR反.
- 使用Mozilla DeepSpeech进行实时转录精度反.
- 引入了噪音,以确保参与者积极地为ASR.产生更清晰的语言.
- 声学测量和语音语言病理学家的评分评估了语音清晰度和可理解性.
主要成果:
- 通过ASR反产生的语言比习惯性语言更清晰,更易于理解.
- 由于ASR引起的清晰语音的声学特征,如元音区分和说话速度,类似于传统的清晰语音.
- 个别的语音特征与ASR准确性,清晰度和可理解性有不同的相关性.
结论:
- ASR反成功地在健康的说话者中引起了清晰的语言,与明确的清晰语言指令相比.
- 这些发现支持ASR作为家庭语音治疗实践的工具的潜力.
- 需要进一步的研究来调查ASR反在患有脱节性关节炎的个体中的有效性.
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