Model-based speech enhancement for automotive applications

In this contribution we present a new approach for speech signal enhancement that improve a distorted speech signal in low SNR scenarios (at least if only a portion of the frequency range is highly disturbed). The approach consists of three algorithmic parts: a standard noise suppression unit, a partial speech reconstruction unit, and a time-frequency selective mixing unit. The basic idea of partial speech reconstruction is first to detect time-frequency areas where the SNR is still acceptable. These areas are used to extract relevant signal features such as the pitch frequency or the spectral envelope. Based on pre-trained signal models and the extracted features, the signal is reconstructed in a second stage. Finally, the reconstructed and the conventionally enhanced signals are mixed in a time-frequency selective manner. Subjective and objective tests indicate that a significant quality improvement is possible compared to conventional schemes - especially in high noise conditions.