Investigation of Text-Independent Speaker Verification by Support Vector Machine-Based Machine Learning Approaches Article Swipe
Odin Kohler
,
Masudul H. Imtiaz
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14050963
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14050963
Speaker verification is a common issue that has enumerable biomedical security applications. Speaker verification comes in two different forms: text-independent and text-dependent. Each of these forms can be implemented via many different machine learning and deep learning techniques. From our research, we found that there is significantly less work implementing text-independent speaker verification using machine learning techniques than there is using deep learning techniques. Because of this gap, we were motivated to build our own SVM and CNN model for text-independent speaker verification and compare them to other systems using SVMs or deep learning techniques. We limited ourselves to SVMs because they are commonly used for speech recognition and achieved very high accuracies. The main motivation behind this was two-fold. The first reason is to demonstrate that SVMs can and have been successfully used for text-independent speaker verification at a level comparable to deep learning techniques; the second reason is to make work using SVMs for text-independent speaker verification more accessible so it can be expanded upon easily. The analysis and comparison conducted in this paper will demonstrate how SVMs achieve results comparable to deep learning techniques and allow future researchers to more easily find SVMs used for text-independent speaker verification and derive a sense of what is being implemented in the field.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics14050963
- https://www.mdpi.com/2079-9292/14/5/963/pdf?version=1740713781
- OA Status
- gold
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408051821
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4408051821Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics14050963Digital Object Identifier
- Title
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Investigation of Text-Independent Speaker Verification by Support Vector Machine-Based Machine Learning ApproachesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-28Full publication date if available
- Authors
-
Odin Kohler, Masudul H. ImtiazList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics14050963Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-9292/14/5/963/pdf?version=1740713781Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2079-9292/14/5/963/pdf?version=1740713781Direct OA link when available
- Concepts
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Speaker verification, Support vector machine, Computer science, Relevance vector machine, Artificial intelligence, Structured support vector machine, Machine learning, Speech recognition, Speaker recognition, Natural language processing, Pattern recognition (psychology)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.achieved | 109 |
| abstract_inverted_index.analysis | 169 |
| abstract_inverted_index.commonly | 103 |
| abstract_inverted_index.expanded | 165 |
| abstract_inverted_index.learning | 33, 36, 55, 62, 93, 144, 185 |
| abstract_inverted_index.security | 10 |
| abstract_inverted_index.conducted | 172 |
| abstract_inverted_index.different | 17, 31 |
| abstract_inverted_index.motivated | 70 |
| abstract_inverted_index.ourselves | 97 |
| abstract_inverted_index.research, | 40 |
| abstract_inverted_index.two-fold. | 119 |
| abstract_inverted_index.accessible | 160 |
| abstract_inverted_index.biomedical | 9 |
| abstract_inverted_index.comparable | 141, 182 |
| abstract_inverted_index.comparison | 171 |
| abstract_inverted_index.enumerable | 8 |
| abstract_inverted_index.motivation | 115 |
| abstract_inverted_index.techniques | 56, 186 |
| abstract_inverted_index.accuracies. | 112 |
| abstract_inverted_index.demonstrate | 125, 177 |
| abstract_inverted_index.implemented | 28, 209 |
| abstract_inverted_index.recognition | 107 |
| abstract_inverted_index.researchers | 190 |
| abstract_inverted_index.techniques. | 37, 63, 94 |
| abstract_inverted_index.techniques; | 145 |
| abstract_inverted_index.implementing | 49 |
| abstract_inverted_index.successfully | 132 |
| abstract_inverted_index.verification | 1, 13, 52, 82, 137, 158, 200 |
| abstract_inverted_index.applications. | 11 |
| abstract_inverted_index.significantly | 46 |
| abstract_inverted_index.text-dependent. | 21 |
| abstract_inverted_index.text-independent | 19, 50, 80, 135, 156, 198 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.02256015 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |