Title: Multi-speaker Recognition in Cocktail Party Problem,
Abstract: This paper proposes an original statistical decision theory to accomplish a
multi-speaker recognition task in cocktail party problem. This theory relies on
an assumption that the varied frequencies of speakers obey Gaussian
distribution and the relationship of their voiceprints can be represented by
Euclidean distance vectors. This paper uses Mel-Frequency Cepstral Coefficients
to extract the feature of a voice in judging whether a speaker is included in a
multi-speaker environment and distinguish who the speaker should be. Finally, a
thirteen-dimension constellation drawing is established by mapping from
Manhattan distances of speakers in order to take a thorough consideration about
gross influential factors.