We present a novel non-iterative and rigorously motivated approach for estimating hidden Markov models (HMMs) and factorial hidden Markov models (FHMMs) of high-dimensional signals. Our approach utilizes the asymptotic properties of a spectral, graph-based approach for dimensionality reduction and manifold learning, namely the diffusion framework. We exemplify our approach by applying it to the problem of single microphone speech separation, where the log-spectra of two unmixed speakers are modeled as HMMs, while their mixture is modeled as an FHMM. We derive two diffusion-based FHMM estimation schemes. One of which is experimentally shown to provide separation results that compare with contemporary speech separation approaches based on HMM. The second scheme allows a reduced computational burden.
CITATION STYLE
Yeminy, Y. R., Keller, Y., & Gannot, S. (2016). Single microphone speech separation by diffusion-based HMM estimation. Eurasip Journal on Audio, Speech, and Music Processing, 2016(1). https://doi.org/10.1186/s13636-016-0094-9
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