Probabilistic Inference of Speech Signals from Phaseless Spectrograms

Many techniques for complex speech processing such as denoising and deconvolution, time/frequency warping, multiple speaker separation, and multiple microphone analysis operate on sequences of short-time power spectra (spectrograms), a representation which is often well-suited to these tasks. However, a significant problem with algorithms that manipulate spectrograms is that the output spectrogram does not include a phase component, which is needed to create a time-domain signal that has good perceptual quality. Here we describe a generative model of time-domain speech signals and their spectrograms, and show how an efficient optimizer can be used to find the maximum a posteriori speech signal, given the spectrogram. In contrast to techniques that alternate between estimating the phase and a spectrally-consistent signal, our technique directly infers the speech signal, thus jointly optimizing the phase and a spectrally-consistent signal. We compare our technique with a standard met...

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Kannan Achan, Sam T. Roweis, Brendan J. Frey
• Keywords: NIPS 2003, NIPS 2007, Perceptual Quality, Posteriori Speech Signal, Speech Signal