When a speech application is employed in a crowded environment, the user's voice superposes with many interfering voices. This babble noise is a challenge for many speech processing algorithms since assumptions like stationarity of the noise or a good SNR may not be valid. In this contribution, characteristics of babble noise are discussed and distinctive features are summarized that allow for distinguishing the desired foreground speech from background noise. In particular, the kurtosis of a signal is identified as a good measure to detect the presence of speech in babble noise. Furthermore, a babble noise suppression system is introduced that employs a speech detector to dynamically control the aggressiveness of noise suppression. It is desirable that the residual noise after processing the signal is perceived as more pleasant by human listeners. To evaluate the improvements, a subjective listening test is conducted. Speech distortions are quantified using an objective measure.
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