Speech pause detection for noise spectrum estimation by tracking power envelope dynamics

Abstract

A speech pause detection algorithm is an important and sensitive part of most single-microphone noise reduction schemes for enhancement of speech signals corrupted by additive noise as an estimate of the background noise is usually determined when speech is absent. An algorithm is proposed which detects speech pauses by adaptively tracking minima in a noisy signal's power envelope both for the broadband signal and for the high-pass and low-pass filtered signal. In poor signal-to-noise ratios (SNRs), the proposed algorithm maintains a low false-alarm rate in the detection of speech pauses while the standardized algorithm of ITU G.729 shows an increasing false-alarm rate in unfavorable situations. These characteristics are found with different types of noise and indicate that the proposed algorithm is better suited to be used for noise estimation in noise reduction algorithms, as speech deteriorations may thus be kept at a low level. It is shown that in connection with the Ephraim-Malah noise reduction scheme [1], the speech pause detection performance can even be further increased by using the noise-reduced signal instead of the noisy signal as input for the speech pause decision unit.

Fuzzy Logic Speech/Non-speech Discrimination for Noise Robust Speech Processing

“…The working points of the ITU-T G.729, ETSI AMR and ETSI AFE VADs are also included. The results show improvements in speech detection accuracy over standard VADs and a representative set of recently reported VAD algorithms [20,21,19,6].…”

“…The non-speech hit rate (HR0) and the false alarm rate (FAR0= 100-HR1) were determined for each noisy condition being the actual speech frames and actual speech pauses determined by hand-labelling the database on the close-talking microphone. Figure 5 shows the ROC curves of the proposed VAD and other frequently referred algorithms [20,21,19,6] for recordings from the distant microphone in quiet and high noisy conditions. The working points of the ITU-T G.729, ETSI AMR and ETSI AFE VADs are also included.…”

“…The analysis is based on the ROC curves [19], a frequently used methodology to describe the VAD error rate. The Spanish SDC database [18] was used.…”

Effect of MFCC Based Features for Speech Signal Alignments

“…If the input speech produced by the alike speaker or similar environmental condition from the reference patterns, the system is likely to find the correct pattern otherwise not [13], [14]. The few valuable approaches used for feature extraction are: full-band and subband energies [15], spectrum divergence between speech signal and noise in background [16], pitch estimation [17], zero crossing rate [18], and higher-order statistics [19], [20], [21], [22]. However, using longterm speech information [23], [24] has shown considerable benefits for identifying speech signal in noisy environments.…”

New Advances in Voice Activity Detection using HOS and Optimization Strategies

“…During the last decade, numerous researchers have developed different strategies for detecting speech on a noisy signal (Sohn et al, 1999), (Cho & Kondoz 2001) and have evaluated the influence of the VAD effectiveness on the performance of speech processing systems (Bouquin-Jeannes & Faucon, 1995) (see also the preceding chapter about VAD). Most of them have focussed on the development of robust algorithms with special attention on the derivation and study of noise robust features and decision rules (Woo et al, 2000), (Li et al, 2002), (Marzinzik & Kollmeier, 2002), (Sohn et al, 1999). The different approaches include those based on energy thresholds (Woo et al, 2000), pitch detection (Chengalvarayan, 1999), spectrum analysis (Marzinzik & Kollmeier, 2002), zero-crossing rate (ITU, 1996), periodicity measures (Tucker, 1992) or combinations of different features (ITU, 1996), (ETSI, 1999).…”