Is cosine similarity a doog measure in speaker recognition?
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Cosine similarity is a commonly used measure in speaker recognition systems. While it offers computational efficiency, recent research suggests that alternative distance metrics can outperform cosine similarity in certain scenarios. For instance, Mahalanobis distance scoring with metric learning algorithms has shown improved performance over cosine similarity in speaker verification tasks. Additionally, techniques like cosine similarity learning frameworks have been developed to enhance equal-error-rate (EER) performance by minimizing overlap regions and controlling variance. Moreover, studies have explored optimizing cosine similarity through orthogonal and diagonal matrices to reduce equal error rates significantly. Therefore, while cosine similarity remains a widely used measure, advancements in distance metrics like Mahalanobis distance and cosine similarity learning frameworks have demonstrated superior performance in speaker recognition tasks.
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| Papers (4) | Insight |
|---|---|
Open access 01 Jan 2015 | The paper proposes using Cosine dissimilarity in Mean Shift algorithm for speaker clustering, showing promise in speaker recognition tasks, indicating that Cosine similarity is a beneficial measure. |
02 Sep 2018 6 Citations | Cosine similarity is utilized effectively in speaker verification, enhancing equal-error-rate performance by minimizing overlap through distance optimization and regularization techniques, as demonstrated in the research. |
Open access•Proceedings Article 01 Sep 2013 | The paper introduces a new generalized cosine similarity optimization technique for speaker recognition, showing significant equal error rate reduction, indicating that cosine similarity is effective in speaker recognition systems. |
13 Nov 2015 | The paper proposes a Mahalanobis distance scoring with KISS metric learning algorithm for speaker recognition, showing improved performance over cosine similarity scoring in speaker recognition tasks. |
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