Reconstructing clean speech from noisy MFCC vectors

Ben Milner, Jonathan Darch, Ibrahim Almajai

Research output: Contribution to conferencePaper

2 Citations (Scopus)


The aim of this work is to reconstruct clean speech solely from a stream of noise-contaminated MFCC vectors, as may be encountered in distributed speech recognition systems. Speech reconstruction is performed using the ETSI Aurora back-end speech reconstruction standard which requires MFCC vectors, fundamental frequency and voicing information. In this work, fundamental frequency and voicing are obtained using maximum a posteriori prediction from input MFCC vectors, thereby allowing speech reconstruction solely from a stream of MFCC vectors. Two different methods to improve prediction accuracy in noisy conditions are then developed. Experimental results first establish that improved fundamental frequency and voicing prediction is obtained when noise compensation is applied. A series of human listening tests are then used to analyse the reconstructed speech quality, which determine the effectiveness of noise compensation in terms of mean opinion scores.
Original languageEnglish
Number of pages4
Publication statusPublished - 2009
Event10th Annual Conference of the International Speech Communication Association (INTERSPEECH) - Brighton, United Kingdom
Duration: 6 Sep 200910 Sep 2009


Conference10th Annual Conference of the International Speech Communication Association (INTERSPEECH)
Country/TerritoryUnited Kingdom

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