Automatic speaker recognition with Multi-resolution Gaussian Mixture models (MR-GMMs)
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| تاريخ النشر: | 2009 |
| مؤلفون آخرون: | , , |
| التنسيق: | Article |
| اللغة: | eng |
| المصدر: | Repositório Institucional da UnB |
| Download full: | http://repositorio.unb.br/handle/10482/11091 https://dx.doi.org/10.5769/J200901001 |
الملخص: | Gaussian Mixture Models (GMMs) are the most widely used technique for voice modeling in automatic speaker recognition systems. In this paper, we introduce a variation of the traditional GMM approach that uses models with variable complexity (resolution). Termed Multi-resolution GMMs (MR-GMMs); this new approach yields more than a 50% reduction in the computational costs associated with proper speaker identification, as compared to the traditional GMM approach. We also explore the noise robustness of the new method by investigating MR-GMM performance under noisy audio conditions using a series of practical identification tests. |
مواد مشابهة: Automatic speaker recognition with Multi-resolution Gaussian Mixture models (MR-GMMs)
- Noise-robust speaker recognition using reduced Multiconditional Gaussian Mixture models
- Influência do fator de aprendizagem na análise perceptivo-auditiva
- Treinamento da performance comunicativa em universitários da área da saúde
- Uma análise probabilística do desempenho de sistemas ASR para rádios e tvs brasileiras
- A musicalidade do sujeito na clínica psicanalítica
- Nasalância de populações falantes do português brasileiro de dois estados distintos