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• W4387008632 abstract "ABSTRACTAutomatic 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.KEYWORDS: Augmentative and Alternative CommunicationLanguage Sample AnalysisSpeech RecognitionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgmentsThe first author created the simulated data set at the State University of New York in Fredonia. We thank Vaneiqua Wilson for transcribing the simulated data for developing the AAC-ASR model, and Zoe Nelson and Jenna O’Donnell for assisting the manuscript preparation.Declaration of interestThe authors report that there are no competing interests to declare.Data Availability StatementThe data that support the findings of this study are available from the corresponding author, SKC, upon reasonable request." @default.
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• W4387008632 date "2023-09-25" @default.
• W4387008632 modified "2023-10-16" @default.
• W4387008632 title "A proof-of-concept study for Automatic speech recognition to transcribe AAC speakers’ speech from high-technology AAC systems." @default.
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• W4387008632 doi "https://doi.org/10.1080/10400435.2023.2260860" @default.
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