Multi-speaker Recognition in Cocktail Party Problem
This addresses the problem of identifying multiple speakers in noisy environments for applications like audio processing, but appears incremental as it builds on existing feature extraction methods with a new statistical framework.
The paper tackles multi-speaker recognition in cocktail party scenarios by proposing a statistical decision theory based on Gaussian-distributed speaker frequencies and Euclidean distance vectors for voiceprints, using Mel-Frequency Cepstral Coefficients for feature extraction and a thirteen-dimension constellation mapping for comprehensive analysis.
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.