Voice Activity Detection Based on Discriminative Weight Training Incorporating a Spectral Flatness Measure

Efficient voice activity detection algorithm using long-term spectral flatness measure

This paper proposes a novel and robust voice activity detection (VAD) algorithm utilizing long-term spectral flatness measure (LSFM) which is capable of working at 10 dB and lower signal-to-noise ratios(SNRs). This new LSFM-based VAD improves speech detection robustness in various noisy environments by employing a low-variance spectrum estimate and an adaptive threshold. The discriminative powe...

Erratum to: Efficient voice activity detection algorithm using long-term spectral flatness measure

A New Algorithm for Voice Activity Detection Based on Wavelet Packets (RESEARCH NOTE)

Speech constitutes much of the communicated information; most other perceived audio signals do not carry nearly as much information. Indeed, much of the non-speech signals maybe classified as ‘noise’ in human communication. The process of separating conversational speech and noise is termed voice activity detection (VAD). This paper describes a new approach to VAD which is based on the Wavelet ...

Variable-Rate CELP Based on Subband Flatness - Speech and Audio Processing, IEEE Transactions on

Code-excited linear prediction (CELP) is the predominant methodology for communications quality speech coding below 8 kbps, and several variable-rate CELP schemes have been discussed in the literature, including QCELP, the variable-rate wideband digital cellular mobile radio speech coding standard specified in IS-95. A key component of these speech coders is the detection and classification of ...

A statistical model-based voice activity detection employing minimum classification error technique

In this paper, we apply a discriminative weight training to a statistical model-based voice activity detection (VAD). In our approach, the VAD decision rule is expressed as the geometric mean of optimally weighted likelihood ratios (LRs) based on a minimum classification error (MCE) method. That approach is different from that of previous works in that different weights are assigned to each fre...