Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383746897> ?p ?o ?g. }
Showing items 1 to 69 of
69
with 100 items per page.
- W4383746897 abstract "Musical speech in speech-language pathology rehabilitation is the production of speech following simple musical (rhythmic or melodic) patterns. This type of speech is used to facilitate speech processing in patients. In this study, we examined the performance of current automatic speech recognition (ASR) algorithms in recognizing normal and musical speech. From a first list of 28 identified algorithms, 24 were excluded for reasons such as low accuracy rate, high computational cost, high price, difficulty of use, long runtime, implementation problems. The four algorithms included were those from Amazon Web Services (AWS Transcribe), Google Speech Recognition, IBM Watson and Rev AI. We ran the selected algorithms on 60 sentences recorded under four speech conditions (Melodic; Rhythmic; Regular Slow; and Regular Normal). All algorithms did perfectly in recognizing the normal speech. The two algorithms with the best performance in musical speech (rhythmic and melodic speech) were AWS Transcribe and IBM Watson, both providing recognition accuracy above 98%. When adding moderate level of white noise and reverberation to the stimuli, AWS Transcribe remained with an acceptable (> 70%) or satisfactory (> 95%) ASR performance. These results may guide the development of software that use ASR to enable patients to undergo self-directed sessions of music-based speech-language rehabilitation, such as the melodic intonation therapy for post-stroke aphasia. The possibility to recognize musical speech allows to compare a patient’s performance to corresponding target phrases and provide feedback in the absence of a clinician. Given the recommended high intensity of treatment and the limited availability of speech-language pathologists, such software would be highly valuable to our healthcare systems." @default.
- W4383746897 created "2023-07-11" @default.
- W4383746897 creator A5037297170 @default.
- W4383746897 creator A5084544690 @default.
- W4383746897 date "2023-06-14" @default.
- W4383746897 modified "2023-09-27" @default.
- W4383746897 title "Performance of Speech Recognition Algorithms in Musical Speech used for Speech-Language Pathology Rehabilitation" @default.
- W4383746897 cites W2013426435 @default.
- W4383746897 cites W2019812715 @default.
- W4383746897 cites W2045349670 @default.
- W4383746897 cites W2046499381 @default.
- W4383746897 cites W2065506310 @default.
- W4383746897 cites W2092876913 @default.
- W4383746897 cites W2103248292 @default.
- W4383746897 cites W2128740644 @default.
- W4383746897 cites W2133122164 @default.
- W4383746897 cites W2620947159 @default.
- W4383746897 cites W3216401400 @default.
- W4383746897 doi "https://doi.org/10.1109/memea57477.2023.10171898" @default.
- W4383746897 hasPublicationYear "2023" @default.
- W4383746897 type Work @default.
- W4383746897 citedByCount "0" @default.
- W4383746897 crossrefType "proceedings-article" @default.
- W4383746897 hasAuthorship W4383746897A5037297170 @default.
- W4383746897 hasAuthorship W4383746897A5084544690 @default.
- W4383746897 hasConcept C142362112 @default.
- W4383746897 hasConcept C14999030 @default.
- W4383746897 hasConcept C153349607 @default.
- W4383746897 hasConcept C154945302 @default.
- W4383746897 hasConcept C15744967 @default.
- W4383746897 hasConcept C169760540 @default.
- W4383746897 hasConcept C171250308 @default.
- W4383746897 hasConcept C192562407 @default.
- W4383746897 hasConcept C2779422653 @default.
- W4383746897 hasConcept C28490314 @default.
- W4383746897 hasConcept C41008148 @default.
- W4383746897 hasConcept C43803900 @default.
- W4383746897 hasConcept C558565934 @default.
- W4383746897 hasConcept C70388272 @default.
- W4383746897 hasConceptScore W4383746897C142362112 @default.
- W4383746897 hasConceptScore W4383746897C14999030 @default.
- W4383746897 hasConceptScore W4383746897C153349607 @default.
- W4383746897 hasConceptScore W4383746897C154945302 @default.
- W4383746897 hasConceptScore W4383746897C15744967 @default.
- W4383746897 hasConceptScore W4383746897C169760540 @default.
- W4383746897 hasConceptScore W4383746897C171250308 @default.
- W4383746897 hasConceptScore W4383746897C192562407 @default.
- W4383746897 hasConceptScore W4383746897C2779422653 @default.
- W4383746897 hasConceptScore W4383746897C28490314 @default.
- W4383746897 hasConceptScore W4383746897C41008148 @default.
- W4383746897 hasConceptScore W4383746897C43803900 @default.
- W4383746897 hasConceptScore W4383746897C558565934 @default.
- W4383746897 hasConceptScore W4383746897C70388272 @default.
- W4383746897 hasLocation W43837468971 @default.
- W4383746897 hasOpenAccess W4383746897 @default.
- W4383746897 hasPrimaryLocation W43837468971 @default.
- W4383746897 hasRelatedWork W1483798125 @default.
- W4383746897 hasRelatedWork W2067342401 @default.
- W4383746897 hasRelatedWork W2141919489 @default.
- W4383746897 hasRelatedWork W2162999097 @default.
- W4383746897 hasRelatedWork W2395221787 @default.
- W4383746897 hasRelatedWork W2405158679 @default.
- W4383746897 hasRelatedWork W2794438528 @default.
- W4383746897 hasRelatedWork W3133417262 @default.
- W4383746897 hasRelatedWork W3167007286 @default.
- W4383746897 hasRelatedWork W3207038517 @default.
- W4383746897 isParatext "false" @default.
- W4383746897 isRetracted "false" @default.
- W4383746897 workType "article" @default.