A Survey on Data Selection for Efficient Speech Processing
While recent advances in speech processing have led to substantial performance improvements across diverse tasks, they often demand significantly higher computational costs and resources. To address this efficiency challenge, data selection has emerged as a crucial strategy. This survey provides a c...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11048490/ |
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| author | Abdul Hameed Azeemi Ihsan Ayyub Qazi Agha Ali Raza |
| author_facet | Abdul Hameed Azeemi Ihsan Ayyub Qazi Agha Ali Raza |
| author_sort | Abdul Hameed Azeemi |
| collection | DOAJ |
| description | While recent advances in speech processing have led to substantial performance improvements across diverse tasks, they often demand significantly higher computational costs and resources. To address this efficiency challenge, data selection has emerged as a crucial strategy. This survey provides a comprehensive overview and introduces a unifying taxonomy for data selection methods in speech processing, structured along three key dimensions: selection granularity (sample-level vs. segment-level), selection process (static, dynamic, or active learning), and selection criteria (uncertainty, diversity, or hybrid approaches). Through systematic analysis across major speech tasks, including automatic speech recognition, text-to-speech synthesis, audio anti-spoofing, speaker recognition, and emotion recognition, we evaluate the effectiveness and applicability of diverse data selection strategies. Our analysis reveals that targeted data selection not only alleviates computational burdens but often enhances model robustness and performance by strategically filtering redundant, noisy, or detrimental training examples. By synthesizing insights scattered across disparate speech domains, we identify common principles, highlight task-specific challenges, and reveal emerging research trends. Finally, we outline promising future research directions in data selection for efficient speech processing. |
| format | Article |
| id | doaj-art-e61b5a14642d4568acdfc25a94381607 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e61b5a14642d4568acdfc25a943816072025-08-20T03:14:50ZengIEEEIEEE Access2169-35362025-01-011310923410925310.1109/ACCESS.2025.358239511048490A Survey on Data Selection for Efficient Speech ProcessingAbdul Hameed Azeemi0https://orcid.org/0000-0003-0506-8365Ihsan Ayyub Qazi1Agha Ali Raza2Department of Computer Science, Lahore University of Management Sciences, Lahore, PakistanDepartment of Computer Science, Lahore University of Management Sciences, Lahore, PakistanDepartment of Computer Science, Lahore University of Management Sciences, Lahore, PakistanWhile recent advances in speech processing have led to substantial performance improvements across diverse tasks, they often demand significantly higher computational costs and resources. To address this efficiency challenge, data selection has emerged as a crucial strategy. This survey provides a comprehensive overview and introduces a unifying taxonomy for data selection methods in speech processing, structured along three key dimensions: selection granularity (sample-level vs. segment-level), selection process (static, dynamic, or active learning), and selection criteria (uncertainty, diversity, or hybrid approaches). Through systematic analysis across major speech tasks, including automatic speech recognition, text-to-speech synthesis, audio anti-spoofing, speaker recognition, and emotion recognition, we evaluate the effectiveness and applicability of diverse data selection strategies. Our analysis reveals that targeted data selection not only alleviates computational burdens but often enhances model robustness and performance by strategically filtering redundant, noisy, or detrimental training examples. By synthesizing insights scattered across disparate speech domains, we identify common principles, highlight task-specific challenges, and reveal emerging research trends. Finally, we outline promising future research directions in data selection for efficient speech processing.https://ieeexplore.ieee.org/document/11048490/Speech processingsurveydata selectiondata pruningactive learningcomputational efficiency |
| spellingShingle | Abdul Hameed Azeemi Ihsan Ayyub Qazi Agha Ali Raza A Survey on Data Selection for Efficient Speech Processing IEEE Access Speech processing survey data selection data pruning active learning computational efficiency |
| title | A Survey on Data Selection for Efficient Speech Processing |
| title_full | A Survey on Data Selection for Efficient Speech Processing |
| title_fullStr | A Survey on Data Selection for Efficient Speech Processing |
| title_full_unstemmed | A Survey on Data Selection for Efficient Speech Processing |
| title_short | A Survey on Data Selection for Efficient Speech Processing |
| title_sort | survey on data selection for efficient speech processing |
| topic | Speech processing survey data selection data pruning active learning computational efficiency |
| url | https://ieeexplore.ieee.org/document/11048490/ |
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