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Novel Approaches to Speech Detection in the Processing of Continuous Audio Streams 27 Figure 1. A block diagram showing the derivation of the phoneme-recognition features The procedure for extracting phoneme-recognition features is shown in Figure 1. First, the acoustic representation of a given signal is produced and passed through a simple phoneme recognizer. Then, the transcription output is translated to specified phoneme classes, in the first case to the consonant (C), vowel (V) and silence (S) classes, and in the second case to the voiced (V), unvoiced (U) and silence (S) regions.

The test database was used to compare the different audio representations and approaches in the SNS-segmentation task. This database is part of the audio database of BN shows in Slovene, which is presented in (Žibert & MiheliĀ, 2004). 1 Evaluation measures The SNS-segmentation results were obtained in terms of the percentage of frame-level accuracy. We calculated three different statistics in each case: the percentage of true speech frames identified as speech, the percentage of true non-speech frames identified as nonspeech, and the overall percentage of speech and non-speech frames identified correctly (the overall accuracy).

Rubio, A. (2005a). An Effective Subband OSF-based VAD with Noise Reduction for Robust Speech Recognition, IEEE Transactions on Speech and Audio Processing, Vol. 13, No. 6, pp. 1119-1129. G; Rubio, A. (2006a). Speech/Non-speech Discrimination based on Contextual Information Integrated Bispectrum LRT, IEEE Signal Processing Letters, vol. 13, No. 8, pp. 497-500. C. (2006a). Generalized LRT-based voice activity detector, IEEE Signal Processing Letters, Vol. 13, No. 10, pp. 636-639. C. (2007). Statistical Voice Activity Detection Based on Integrated Bispectrum Likelihood Ratio Tests, to appear in Journal of the Acoustical Society of America.

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Acoustic Pressures in Case of Soap Bubbles by Barus C.

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