Speaker recognition is usually accomplished by building a set of models from speech of a known speaker, training data, and subsequently using a pattern matching algorithm to score the speech from an unknown speaker, test data. In this paper we discard the notion of train and test data in speaker recognition and introduce the multilateral scoring technique. This technique comprises building spea...

In this paper, we introduce an ensemble speaker modeling using a speaker adaptive training (SAT) deep neural network (SAT-DNN). We first train a speaker-independent DNN (SIDNN) acoustic model as a universal speaker model (USM). Based on the USM, a SAT-DNN is used to obtain a set of speaker-dependent models by assuming that all other layers except one speaker-dependent (SD) layer are shared amon...

Unsupervised speaker indexing sequentially detects points where a speaker identity changes in a multi-speaker audio stream, and categorizes each speaker segment, without any prior knowledge about the speakers. This project addresses two challenges: The first relates to sequential speaker change detection. The second relates to speaker modeling in light of the fact that the number/identity of th...

external amplifier was able to detect the sound waves from microphone on speaker and oscilloscope. The maximum amplitude of output speech signal of amplifier is 45 mV, and the maximum output of MEMS microphone is 1.125 µV.