In many speech signal processing applications, voice activity detection (VAD) plays an essential role for separating an audio stream into time intervals that contain speech activity and time intervals where speech is absent. Many features that reflect the presence of speech were introduced in literature. However, to our knowledge, no extensive comparison has been provided yet. In this article, we therefore present a structured overview of several established VAD features that target at different properties of speech. We categorize the features with respect to properties that are exploited, such as power, harmonicity, or modulation, and evaluate the performance of some dedicated features. The importance of temporal context is discussed in relation to latency restrictions imposed by different applications. Our analyses allow for selecting promising VAD features and finding a reasonable trade-off between performance and complexity.

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Speech recognition offers an intuitive and convenient interface to control technical devices. Improvements achieved through ongoing research activities enable the user to handle increasingly complex tasks via speech. For special applications, e.g. dictation, highly sophisticated techniques have been developed to yield high recognition accuracy. Many use cases, however, are characterized by changing conditions such as different speakers or time-variant environments. A manifold of approaches has been published to handle the problem of changes in the acoustic environment or speaker specific voice characteristics by adapting the statistical models of a speech recognizer and speaker tracking. Combining speaker adaptation and speaker tracking may be advantageous, because it allows a system to adapt to more than one user at the same time. The performance of speech controlled systems may be continuously improved over time. In this article we review some techniques and systems for unsupervised speaker tracking which may be combined with speech recognition. We discuss a unified view on speaker identification and speech recognition embedded in a selflearning system. The latter adapts individually to its main users without requiring additional interventions of the user such as an enrollment. Robustness is continuously improved by progressive speaker adaptation. We analyze our evaluation results for a realistic in-car application to validate the evolution of the system in terms of speech recognition accuracy and identification rate.