Design of Scenario-specific Features for Voice Activity Detection and Evaluation for Different Speech Enhancement Applications

Voice activity detection (VAD) aims at identifying presence of speech in a noisy signal. For this purpose, features are extracted: the signal is processed in such a way that certain distinctive properties of human speech are emphasized.

Various features focusing on different speech properties have been introduced with the goal of telling apart speech and noise. A detector finally decides whether speech is present in the signal.

In this thesis, many features for VAD are summarized and classified with respect to
properties of human speech that are exploited. New features are introduced considering speech properties that are typically not taken into account. Since different features represent different aspects of human speech, a combination of multiple features is desirable. By considering advantages and drawbacks of each feature, the final detection result can be improved. Adequate feature combinations may increase the robustness against interferences.

The evaluations in this thesis are dedicated to the requirements of the target application. Some important applications are analyzed with respect to their dependency on VAD results. The importance of accurate VAD results is exemplified for algorithms in an ICC system and for the suppression of babble noise. These applications cover important use cases of VAD with particularly challenging yet contrary conditions. Tried and tested for these rather extreme cases, the
approaches discussed in this thesis are well suited also for other applications with less strict constraints.



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