Voice Activity Detection Based on Augmented Statistical Noise Suppression

Abstract-A new voice activity detection (VAD) algorithm using augmented statistical noise suppression is introduced. Statistical noise suppression is an effective tool for speech processing under noisy conditions. It achieves the best VAD performance when the noise suppression is augmented in various ways. The speech distortion, which is usually a severe side effect of strong noise suppression, does not affect the VAD performance, and the correctly estimated signal power provides accurate detection of speech. The performance of the proposed algorithm is evaluated using CENSREC-1-C public database, and it is confirmed that the proposed algorithm outperforms other algorithms such as the switching Kalman filter-based VAD

Robust Embedded Version of Continuous Speech Recognition Software on Microprocessor

MobileHCI2006: 8th International Conference on Human Computer Interaction with Mobile Devices and Services , September 12-15, 2006, Espoo, Finland.In this paper, we describe new mobile consumer services based on speech technologies to support a new digital/mobile era of ubiquitous communication. First, we report evaluation results of recognition modules for the noise robustness such as speech segmentation, microphone array techniques, and feature normalization modules. We used free Continuous Speech Recognition (CSR) software Julius/Julian as a speech decoder. Second, we have developed an embedded version of the Julius continuous speech recognition software on general-purpose microprocessors. We used T-Engine? as a hardware platform. The technical problems to make Julius for embedded one are computing/process and memory reductions of Julius. We could realize about 2.00 of RTF (Real Time Factor) of speech recognition processing on the condition of 5000-word vocabulary