Least squares signal declipping for robust speech recognition

This paper introduces a novel declipping algorithm based on constrained least-squares minimization. Digital speech signals are often sampled at 16 kHz and classic declipping algorithms fail to accurately reconstruct the signal at this sampling rate due to the scarcity of reliable samples after clipping. The Constrained Blind Amplitude Reconstruction algorithm interpolates missing data points such that the resulting function is smooth while ensuring the inferred data fall in a legitimate range. The inclusion of explicit constraints helps to guide an accurate interpolation. Evaluation of declipping performance is based on automatic speech recognition word error rate and Constrained Blind Amplitude Reconstruction is shown to outperform the current state-of-the-art declipping technology under a variety of conditions. Declipping performance in additive noise is also considered.