Abstract
This work reports experimental results in various speech processing tasks using an application based on the Modular Audio Recognition Framework (MARF) in terms of the best of the available algorithm configurations for each particular task. This study focuses on the tasks of identification of speakers’ as of their gender and accent vs. who they are through machine learning. This work significantly complements a preceding statistical study undertaken only for the text-independent speaker identification.
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References
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Mokhov, S.A. (2008). Choosing Best Algorithm Combinations for Speech Processing Tasks in Machine Learning Using MARF. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_21
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DOI: https://doi.org/10.1007/978-3-540-68825-9_21
Publisher Name: Springer, Berlin, Heidelberg
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