Parameter Tuning-Free Missing-Feature Reconstruction for Robust Sound Recognition

Qi Liu, Jibin Wu

Abstract: With the advent of the deep neural network, automatic speech recognition (ASR) has seen significant improvements in recent years. However, ASR performance degrades rapidly when the acoustic environment, such as communication channels or noise backgrounds, differ from those of training data. In the missing feature approach to speech processing, the unreliable feature components are identified and reconstructed to overcome signal degradation and the mismatch of the acoustic environment. To reduce the model dependency, we investigate the matrix completion technique in missing feature reconstruction tasks. However, most of the matrix completion techniques require a priori tuning parameters, e.g., target rank, which is hard to determine in practice. In this work, we propose a matrix completion method based on matrix factorization for the missing-feature reconstruction task, that does not require model training nor parameter tuning. Experiments show superior feature reconstruction performance and computational efficiency in both speech recognition and environmental sound classification tasks.