Neighbour selection and adaptation for rapid speaker-dependent ASR

Nallasamy, Udhyakumar; Fuhs, Mark C.; Woszczyna, Monika; Metze, Florian; Schultz, Tanja

doi:10.1109/asru.2013.6707706

Cited by 3 publications

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Feasibility of Automatic Speech Recognition for Providing Feedback During Tablet-Based Treatment for Apraxia of Speech Plus Aphasia

Ballard

Etter

Shen

et al. 2019

Am J Speech Lang Pathol

View full text Add to dashboard Cite

Purpose Individuals with neurogenic speech disorders require ongoing therapeutic support to achieve functional communication goals. Alternative methods for service delivery, such as tablet-based speech therapy applications, may help bridge the gap and bring therapeutic interventions to the patient in an engaging way. The purpose of this study was to evaluate an iPad-based speech therapy app that uses automatic speech recognition (ASR) software to provide feedback on speech accuracy to determine the ASR's accuracy against human judgment and whether participants' speech improved with this ASR-based feedback. Method Five participants with apraxia of speech plus aphasia secondary to stroke completed an intensive 4-week at-home therapy program using a novel word training app with built-in ASR. Multiple baselines across participants and behaviors designs were employed, with weekly probes and follow-up at 1 month posttreatment. Four sessions a week of 100 practice trials each were prescribed, with 1 being clinician-run and the remainder done independently. Dependent variables of interest were ASR–human agreement on accuracy during practice trials and human-judged word production accuracy over time in probes. Also, user experience surveys were completed immediately posttreatment. Results ASR–human agreement on accuracy averaged ~80%, which is a common threshold applied for interrater agreement. All participants demonstrated improved word production accuracy over time with the ASR-based feedback and maintenance of gains after 1 month. All participants reported enjoying using the app with support of a speech pathologist. Conclusion For these participants with apraxia of speech plus aphasia due to stroke, satisfactory gains were made in word production accuracy with an app-based therapy program providing ASR-based feedback on accuracy. Findings support further testing of this ASR-based approach as a supplement to clinician-run sessions to assist clients with similar profiles in achieving higher amount and intensity of practice as well as empowering them to manage their own therapy program. Supplemental Material https://doi.org/10.23641/asha.8206628

show abstract

Feasibility of Automatic Speech Recognition for Providing Feedback During Tablet-Based Treatment for Apraxia of Speech Plus Aphasia

Ballard

Etter

Shen

et al. 2019

Am J Speech Lang Pathol

View full text Add to dashboard Cite

show abstract

Automatic selection of speakers for improved acoustic modelling: recognition of disordered speech with sparse data

Christensen

Casanueva

Cunningham

et al. 2014

2014 IEEE Spoken Language Technology Workshop (SLT)

View full text Add to dashboard Cite

Improving language-universal feature extraction with deep maxout and convolutional neural networks

Miao¹,

Metze²

2014

Interspeech 2014

View full text Add to dashboard Cite

When deployed in automated speech recognition (ASR), deep neural networks (DNNs) can be treated as a complex feature extractor plus a simple linear classifier. Previous work has investigated the utility of multilingual DNNs acting as language-universal feature extractors (LUFEs). In this paper, we explore different strategies to further improve LUFEs. First, we replace the standard sigmoid nonlinearity with the recently proposed maxout units. The resulting maxout LUFEs have the nice property of generating sparse feature representations. Second, the convolutional neural network (CNN) architecture is applied to obtain more invariant feature space. We evaluate the performance of LUFEs on a cross-language ASR task. Each of the proposed techniques results in word error rate reduction compared with the existing DNN-based LUFEs. Combining the two methods together brings additional improvement on the target language.

show abstract

Neighbour selection and adaptation for rapid speaker-dependent ASR

Cited by 3 publications

References 18 publications

Feasibility of Automatic Speech Recognition for Providing Feedback During Tablet-Based Treatment for Apraxia of Speech Plus Aphasia

Feasibility of Automatic Speech Recognition for Providing Feedback During Tablet-Based Treatment for Apraxia of Speech Plus Aphasia

Automatic selection of speakers for improved acoustic modelling: recognition of disordered speech with sparse data

Improving language-universal feature extraction with deep maxout and convolutional neural networks

Contact Info

Product

Resources

About