The different VAD methods include those based on energy thresholds [19], pitch selleck detection [22], spectrum analysis [21], zero-crossing rate [23], periodicity measure [24], higher order statistics in the LPC residual domain [25] or combinations of different features [26,27]. Voice activity detection techniques relying on artificial intelligence and soft computing have emerged in recent years to surmount the problem of VAD. These techniques include the use of support vector machine [28], neural networks [29], and fuzzy logic [30]. These classification strategies practically fail to solve the problem due to the non-stationary nature of both the speech and the background noise.In speech processing systems, it is important to determine the presence of speech periods in a given signal.
This task can be viewed as a statistical problem with a purpose of determining to which class a given signal belongs. The decision is based on an observation vector, usually called a feature vector, which serves as the input to a decision rule that assigns a sample vector to one of the given classes. The classification task is often quite difficult due to the increasing level of background noise, which degrades the classifier effectiveness, thus leading to detection errors. The choice of an adequate feature vector for signal detection followed by a robust decision rule is a challenging problem for VADs operating in noisy environments. Many VAD algorithms are effective in a large number of applications, however, they fail to detect properly, mainly because of the loss of discriminating power of the decision rule when the signal to noise ratio (SNR) is severely low [23,26].
For instance, a simple energy level detector can work effectively in high SNR levels, but would fail significantly when the SNR becomes low. In non-stationary noise environments, the use of VAD is more critical since it is needed to update the continuously varying noise statistics which have a direct impact on the system performance due to possible misclassification errors. Desirable aspects of VAD algorithms include the following.-A good decision rule: A physical property Drug_discovery of speech that can be exploited small molecule to give consistent and accurate judgment in classifying seg
An important capability for service robots working in indoor environments is their ability to categorize the different places where they are located. Place categorization has many applications in service robots. It is mainly used in semantic mapping, where acquired maps of the environment are extended with information about the type of each place allowing high level conceptual representations of environments [1�C6].