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Conventional speaker recognition systems use Gaussian Mixture Models (GMM) to model a speaker's voice based on the speaker's acoustic characteristics. This method is categorized as a non-discriminative training process, as the model-building process does not take into account the negative examples of the speaker. To increase the discriminative properties of a GMM for each speaker, a new approach that includes both positive and negative examples during the speaker training process is proposed. In this approach, speaker models are trained by moving the mixture model's means in such a way as to maximize the likelihood of speaker data while also minimizing the likelihood of negative examples for the speaker.
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