A survey and evaluation of voice activity detection algorithms - Meduri, Sameeraj;Ananth, Rufus
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With the recent advances in speech signal processing techniques, the need to detect the presence of speech accurately in the incoming signal under different noise environments has become a major concern of the industry. The separation of speech segment from the non-speech segment in an audio signal is achieved using a Voice Activity Detectors (VAD). VAD s are a class signal processing methods that detects the presence or absence of speech in short segments of audio signal. A VAD has a pivotal role as a preprocessing block in wide range of speech applications. An integrated VAD in speech…mehr

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Produktbeschreibung
With the recent advances in speech signal processing techniques, the need to detect the presence of speech accurately in the incoming signal under different noise environments has become a major concern of the industry. The separation of speech segment from the non-speech segment in an audio signal is achieved using a Voice Activity Detectors (VAD). VAD s are a class signal processing methods that detects the presence or absence of speech in short segments of audio signal. A VAD has a pivotal role as a preprocessing block in wide range of speech applications. An integrated VAD in speech communication system, improves channel capacity, reduces co-channel interference and power consumption in portable electronic devices in cellular radio systems and allows simultaneous voice and data applications in multimedia communications. In slowly varying non-stationary environments where speech is corrupted by noise, a VAD is used to learn noise characteristics and estimate the noise spectrum. Furthermore, the output from the VAD is helpful in improving the performance of the speech recognition systems which applies a technique called non-speech frame dropping (FD) to reduce the insertion error
Autorenporträt
Completed post graduation from BLEKINGE TEKNISKA HOGSKOLAN sweden with emphasis on Signal Processingand presently working with on cognitive neural sciences