Speech Bandwidth Extension Using Articulatory Features

In this paper, we present a technique for bandwidth extension (BWE) of a narrowband (0 4 kHz) signal using articulatory features. The proposed technique recovers high-band components (4 8 kHz) through Gaussian mixture regression (GMR) on both the acoustic and articulatory features from the X-ray Microbeam (XRMB) speech production database. The Gaussian mixture model (GMM) that is based on acoustic and articulatory features is initialized using k-means and iteratively trained using expectation-maximization (EM) algorithm. BWE experiments were run using data files from different speakers in XRMB as train and test data. Time-frequency plots of speech recovered by different training methods are presented in order to show that articulatory trajectories are helpful in characterizing high frequencied consonants in speech. Finally, we confirm our hypothesis that using GMM with articulation gives better recovery rate is true by performing Student’s t-test on SNR data between original and recovered speech.