1 research outputs found
Speech Separation Using Gain-Adapted Factorial Hidden Markov Models
We present a new probabilistic graphical model which generalizes factorial
hidden Markov models (FHMM) for the problem of single-channel speech separation
(SCSS) in which we wish to separate the two speech signals and
from a single recording of their mixture using the trained
models of the speakers' speech signals. Current techniques assume the data used
in the training and test phases of the separation model have the same loudness.
In this paper, we introduce GFHMM, gain adapted FHMM, to extend SCSS to the
general case in which , where and are unknown
gain factors. GFHMM consists of two independent-state HMMs and a hidden node
which model spectral patterns and gain difference, respectively. A novel
inference method is presented using the Viterbi algorithm and quadratic
optimization with minimal computational overhead. Experimental results,
conducted on 180 mixtures with gain differences from 0 to 15~dB, show that the
proposed technique significantly outperforms FHMM and its memoryless
counterpart, i.e., vector quantization (VQ)-based SCSS