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Updated: Jul 19, 2026
09:09
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
Published on: September 27, 2024
[Speaker identification based on Mel frequency cepstrum coefficient and complexity measure]
Dawei Mao1, Hua Cao, Hamit Murat
1College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China. maodawei@163.com
Summary
Combining Mel frequency cepstral coefficients (MFCC) with Lempel-Ziv Complexity significantly improves speaker identification accuracy. This novel approach enhances both text-dependent and text-independent recognition rates for speaker verification systems.
Area of Science:
- Speech processing and machine learning.
- Biometric security systems.
- Pattern recognition algorithms.
Context:
- Traditional speaker identification relies on parameters like Mel frequency cepstral coefficients (MFCC).
- Existing methods face challenges in achieving high accuracy across diverse conditions.
- Evaluating novel feature parameters is crucial for advancing speaker recognition technology.
Purpose:
- To investigate the efficacy of integrating Lempel-Ziv Complexity with MFCC for speaker identification.
- To quantitatively assess the performance improvement offered by this combined feature set.
- To demonstrate the potential of Lempel-Ziv Complexity as a valuable parameter in speaker recognition.
Summary:
- Initially, only MFCC was employed as the feature parameter for speaker identification.
- Subsequently, Lempel-Ziv Complexity was combined with MFCC to create a more robust feature set.
- The combined approach resulted in a significant performance increase, boosting text-dependent recognition from 42% to 80% and text-independent recognition from 60% to 72% for 50 speakers.
Impact:
- Lempel-Ziv Complexity emerges as a promising new parameter for enhancing speaker identification systems.
- The findings suggest a pathway for developing more accurate and reliable biometric authentication methods.
- This research contributes to the field of signal processing by introducing an effective feature combination for speaker verification.
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