Local peak enhancement combined with noise reduction algorithms for robust automatic speech recognition in automobiles
Osamu Ichikawa, Takashi Fukuda, Masafumi Nishimura · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
The accuracy of automatic speech recognition in automobiles is significantly degraded in very low SNR (Signal to Noise Ratio) situations such as “Fan high” or “Window open”. In such cases, speech signals are often buried in broadband noise. In this paper, we propose a novel approach for such situations that utilizes harmonic structures in the human voice. It pursues two objectives. (1) Unlike comb filtering, it should not rely on F0 detection or voiced/unvoiced detection, since they are not accurate enough in noisy environments. (2) It should work with existing noise reduction algorithms. In our new approach, an observed power spectrum is directly converted into a filter for speech enhancement by retaining only the local peaks considered to be harmonic structures. In our experiments, we reduced the word error rate significantly in realistic automobile environments, and our approach showed further improvements when used with existing noise reduction algorithms.