An improved endpoint detector for isolated word recognition

Detection of Rain in Acoustic Recordings of the Environment

“…The spectrum was smoothed with a moving average window of width three. Spectrograms were "noise reduced" using a modification of adaptive level equalization [8] applied to every frequency bin independently [9]. Adaptive level equalization has the effect of removing continuous background acoustic activity and setting that level to zero amplitude.…”

Environmental Sound Recognition Using Spectrogram Image Features

“…End point detection and silence removal are some of the well-known techniques for segmenting the speech. Lamel et al [8] describes endpoint detector for isolated word segmentation, where a histogram of the low 10db of log energy levels are considered to estimate the background noise. Tzanetakis and Cook [9] describe a methodology for temporal segmentation using different features such as Spectral features, MFCCs, LPC coefficients and pitch.…”

A vocal response time system for use with sentence verification tasks

“…With this technique, both the energy and zero-crossing rate of the signal are used to define the end points of an utterance. This technique is simpler to use than more advanced techniques, such as those specified by Lamel, Rabiner, Rosenburg, and Wilpon (1981); Wilpon and Rabiner (1987); Savoji (1989); and more recently by Junqua, Mak, and Reaves (1994). Some modifications were made to improve performance under the conditions used in our SVT studies at the Hearing Health Care Research Unit.…”