Towards Automated Assessment of Stuttering and Stuttering Therapy

Bayerl, Sebastian P.; Hönig, Florian; Reister, Joelle; Riedhammer, Korbinian

doi:10.1007/978-3-030-58323-1_42

Cited by 9 publications

(16 citation statements)

References 17 publications

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“…Each event-based segment varies in duration. Databases that have been established subsequently have segmented speech into fixed length intervals (usually 3 s; 3-s) referred to here as ‘interval-based’ procedures ( Lea et al, 2021 ; Bayerl et al, 2022a ). They provide labels for each interval (‘fluent’ or ‘stuttered’ or, in some cases, ‘fluent’ and the specific type of stutter).…”

Section: Introductionmentioning

confidence: 99%

Comparison of performance of automatic recognizers for stutters in speech trained with event or interval markers

Barrett,

Tang,

Howell

2024

Front. Psychol.

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IntroductionAutomatic recognition of stutters (ARS) from speech recordings can facilitate objective assessment and intervention for people who stutter. However, the performance of ARS systems may depend on how the speech data are segmented and labelled for training and testing. This study compared two segmentation methods: event-based, which delimits speech segments by their fluency status, and interval-based, which uses fixed-length segments regardless of fluency.MethodsMachine learning models were trained and evaluated on interval-based and event-based stuttered speech corpora. The models used acoustic and linguistic features extracted from the speech signal and the transcriptions generated by a state-of-the-art automatic speech recognition system.ResultsThe results showed that event-based segmentation led to better ARS performance than interval-based segmentation, as measured by the area under the curve (AUC) of the receiver operating characteristic. The results suggest differences in the quality and quantity of the data because of segmentation method. The inclusion of linguistic features improved the detection of whole-word repetitions, but not other types of stutters.DiscussionThe findings suggest that event-based segmentation is more suitable for ARS than interval-based segmentation, as it preserves the exact boundaries and types of stutters. The linguistic features provide useful information for separating supra-lexical disfluencies from fluent speech but may not capture the acoustic characteristics of stutters. Future work should explore more robust and diverse features, as well as larger and more representative datasets, for developing effective ARS systems.

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Section: Introductionmentioning

confidence: 99%

Comparison of performance of automatic recognizers for stutters in speech trained with event or interval markers

Barrett,

Tang,

Howell

2024

Front. Psychol.

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“…2.1.3 Dysfluent Speech Recognition. Technical work on improving speech assistants for PWS has focused on ASR models [8,23,31,35,50,51,61], stuttering detection [43], dysfluency detection or classification [22,40,42,48,56], clinical assessment [11], and dataset development [12,37,42,55]. Shonibare et al [61] and Mendelev et al [50] investigate training end-to-end RNN-T ASR models on speech from PWS.…”

Section: Overview Of Speech Recognition Systemsmentioning

confidence: 99%

“…Most VAs use a wake word like "Alexa, " "OK Google, " or "Hey Siri" for invocation, which can be difficult for some PWS to use. Almost half of participants reported that these wake words 'always' (11 ) reported that low utility prevented them from using dictation more often, and 12 (26.7%) felt that they use more refined language when they type than when they speak.…”

Section: Voice Assistant Challengesmentioning

confidence: 99%

From User Perceptions to Technical Improvement: Enabling People Who Stutter to Better Use Speech Recognition

Lea¹,

Huang²,

Tooley³

et al. 2023

Preprint

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Consumer speech recognition systems do not work as well for many people with speech differences, such as stuttering, relative to the rest of the general population. However, what is not clear is the degree to which these systems do not work, how they can be improved, or how much people want to use them. In this paper, we first address these questions using results from a 61-person survey from people who stutter and find participants want to use speech recognition but are frequently cut off, misunderstood, or speech predictions do not represent intent. In a second study, where 91 people who stutter recorded voice assistant commands and dictation, we quantify how dysfluencies impede performance in a consumer-grade speech recognition system. Through three technical investigations, we demonstrate how many common errors can be prevented, resulting in a system that cuts utterances off 79.1% less often and improves word error rate from 25.4% to 9.9%. CCS CONCEPTS• Human-centered computing → Empirical studies in accessibility.

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“…Die zur Bewertung von Stottern eingesetzten silbenbasierten Tests Stuttering Severity Instrument (SSI-4) und Speech Efficiency Score können insoweit automatisiert werden, dass ein Spracherkenner zunächst eine silbenbasierte Transkription erstellt, welche im Nachgang auf Stottern untersucht wird. Dies ist zwar in Ansätzen möglich, kommt aber aufgrund der Variabilität der Stotterphänomene schnell an seine Grenzen 20 ; die Auswertung durch einen Therapeuten ist aber ebenso zeitaufwändig wie fehleranfällig.…”

Section: Automatisierung Von Testsunclassified