SPEECH ENHANCEMENT USING DISCRIMINATIVE RANDOM FIELDS

Saligrama, Ajey and Ranjani, HG and Shankar, HN and Muralishankar, R (2015) SPEECH ENHANCEMENT USING DISCRIMINATIVE RANDOM FIELDS. In: IEEE Region 10 Conference, NOV 01-04, 2015, Macao, PEOPLES R CHINA.

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Abstract

Speech enhancement in stationary noise is addressed using the ideal channel selection framework. In order to estimate the binary mask, we propose to classify each time-frequency (T-F) bin of the noisy signal as speech or noise using Discriminative Random Fields (DRF). The DRF function contains two terms - an enhancement function and a smoothing term. On each T-F bin, we propose to use an enhancement function based on likelihood ratio test for speech presence, while Ising model is used as smoothing function for spectro-temporal continuity in the estimated binary mask. The effect of the smoothing function over successive iterations is found to reduce musical noise as opposed to using only enhancement function. The binary mask is inferred from the noisy signal using Iterated Conditional Modes (ICM) algorithm. Sentences from NOIZEUS corpus are evaluated from 0 dB to 15 dB Signal to Noise Ratio (SNR) in 4 kinds of additive noise settings: additive white Gaussian noise, car noise, street noise and pink noise. The reconstructed speech using the proposed technique is evaluated in terms of average segmental SNR, Perceptual Evaluation of Speech Quality (PESQ) and Mean opinion Score (MOS).

Item Type:	Conference Proceedings
Series.:	TENCON IEEE Region 10 Conference Proceedings
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	08 Oct 2016 07:30
Last Modified:	08 Oct 2016 07:30
URI:	http://eprints.iisc.ac.in/id/eprint/54806

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