A comparison of GMM-HMM and DNN-HMM based pronunciation verification techniques for use in the assessment of childhood apraxia of speech
Abstract
This paper introduces a pronunciation verification method to be used in an automatic assessment therapy tool of child disordered speech. The proposed method creates a phonebased search lattice that is flexible enough to cover all probable mispronunciations. This allows us to verify the correctness of the pronunciation and detect the incorrect phonemes produced by the child. We compare between two different acoustic models, the conventional GMM-HMM and the hybrid DNN-HMM. Results show that the hybrid DNNHMM outperforms the conventional GMM-HMM for all experiments on both normal and disordered speech. The total correctness accuracy of the system at the phoneme level is above 85% when used with disordered speech.
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Most research on automatic aphasia assessment has focused on a single aphasia assessment task using automatic speech recognition techniques and a machine learning framework with a fixed dataset-type.
“Most research on automatic aphasia assessment [5,9,10,12,13,14,18] has focused on a single aphasia assessment task using automatic speech recognition techniques and a machine learning (ML) framework with a fixed dataset-type.”
From Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment (2021) · cites this paper as [5]
Cites
The Percentage of Consonants Correct (PCC) Metric: Extensions and Reliability Data
Shriberg, L. D., Austin, D., Lewis, B. A., McSweeny, J. L., & Wilson, D. L. (1997) · Journal of Speech, Language, and Hearing Research · doi:10.1044/jslhr.4004.708 · Cited by 647 · see citing works
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Shahin, M., Zafar, U., & Ahmed, B. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2959393 · Cited by 62 · see citing works
Self-citationAn Efficient Deep Learning Based Method for Speech Assessment of Mandarin-Speaking Aphasic Patients
Mahmoud, S. S., Kumar, A., Tang, Y., Li, Y., Gu, X., Fu, J., & Fang, Q. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3011104 · Cited by 36 · see citing works
A machine learning based system for the automatic evaluation of aphasia speech
Kohlschein, C., Schmitt, M., Schuller, B., Jeschke, S., & Werner, C. J. (2017) · 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2017.8210766 · Cited by 29 · see citing works
Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment
Mahmoud, S. S., Kumar, A., Li, Y., Tang, Y., & Fang, Q. (2021) · Sensors · doi:10.3390/s21082582 · Cited by 27 · see citing works
“Most research on automatic aphasia assessment [5,9,10,12–14 , 18 ] has focused on a single aphasia assessment task using This paper is organized as follows.”
BackgroundMahmoud, S. S., Pallaud, R. F., Kumar, A., Faisal, S., Wang, Y., & Fang, Q. (2023) · Sensors · doi:10.3390/s23020857 · Cited by 25 · see citing works
- Authors
- Shahin, M., Ahmed, B., McKechnie, J., Ballard, K., & Gutierrez-Osuna, R.
- Venue
- Interspeech 2014
- Publisher
- ISCA
- Published
- 2014-09-14
- Pages
- 1583-1587
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2401692397
- Cited by
- 34
- References
- 1
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 2 · 2021: 8 · 2020: 6 · 2019: 4 · 2018: 3 · 2017: 1 · 2016: 3 · 2014: 1
- FWCI
- 0.98
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Keywords
- childhood apraxia of speech, DNN-HMM, GMM-HMM
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