A proof-of-concept study for automatic speech recognition to transcribe AAC speakers' speech from high-technology AAC systems.
Automatic speech recognition (ASR) is an emerging technology that has been used in recognizing non-typical speech of people with speech impairment and enhancing the language sample transcription process in communication sciences and disorders. However, the feasibility of using ASR for recognizing sp...
| Publicado en: | Assistive Technology Vol. 36; no. 4; pp. 319 - 327 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts tracings Journal Article |
| Publicado: |
Taylor & Francis Ltd
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178068769&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178068769 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10400435 YVP jtl: Assistive Technology issn: 10400435 maglogo: Y pubinfo: dt: 2024 vid: 36 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 178068769 172302506 178068769 178068769 10.1080/10400435.2023.2260860 178068769 ppf: 319 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A proof-of-concept study for automatic speech recognition to transcribe AAC speakers' speech from high-technology AAC systems. aug: au: Chen, Szu-Han Kay Saeli, Conner Hu, Gang affil: Department of Communication Sciences and Disorders, The University of New Hampshire, Durham, New Hampshire, USA sug: subj: Voice Recognition Systems Alternative and Augmentative Communication Speech Sample Human Descriptive Statistics Machine Learning Mean Length of Utterance Speech Acoustics ab: Automatic speech recognition (ASR) is an emerging technology that has been used in recognizing non-typical speech of people with speech impairment and enhancing the language sample transcription process in communication sciences and disorders. However, the feasibility of using ASR for recognizing speech samples from high-tech Augmentative and Alternative Communication (AAC) systems has not been investigated. This proof-of-concept paper aims to investigate the feasibility of using AAC-ASR to transcribe language samples generated by high-tech AAC systems and compares the recognition accuracy of two published ASR models: CMU Sphinx and Google Speech-to-text. An AAC-ASR model was developed that transcribes simulated AAC speaker language samples. The AAC-ASR model's word error rate (WER) was compared with those of CMU Sphinx and Google Speech-to-text. The WER of the AAC-ASR model outperformed (28.6%) compared with CMU Sphinx and Google when tested on the testing files (70.7% and 86.2% retrospectively). Our results demonstrate the feasibility of using the ASR model to automatically transcribe high-technology AAC-simulated language samples to support language sample analysis. Future steps will focus on developing the model with diverse AAC speech training datasets and understanding the speech patterns of individual AAC users to refine the AAC-ASR model. pubtype: Academic Journal doctype: pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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