Voice Activity Detection - Algorithm Overview

Algorithm Overview

The typical design of a VAD algorithm is as follows:

1. There may first be a noise reduction stage, e.g. via spectral subtraction.
2. Then some features or quantities are calculated from a section of the input signal.
3. A classification rule is applied to classify the section as speech or non-speech – often this classification rule finds when a value exceeds a threshold.

There may be some feedback in this sequence, in which the VAD decision is used to improve the noise estimate in the noise reduction stage, or to adaptively vary the threshold(s). These feedback operations improve the VAD performance in non-stationary noise (i.e. when the noise varies a lot).

A representative set of recently published VAD methods formulates the decision rule on a frame by frame basis using instantaneous measures of the divergence distance between speech and noise. The different measures which are used in VAD methods include spectral slope, correlation coefficients, log likelihood ratio, cepstral, weighted cepstral, and modified distance measures.

Independently from the choice of VAD algorithm, we must compromise between having voice detected as noise or noise detected as voice (between false positive and false negative). A VAD operating in a mobile phone must be able to detect speech in the presence of a range of very diverse types of acoustic background noise. In these difficult detection conditions it is often preferable that a VAD should fail-safe, indicating speech detected when the decision is in doubt, to lower the chance of losing speech segments. The biggest difficulty in the detection of speech in this environment is the very low signal-to-noise ratios (SNRs) that are encountered. It may be impossible to distinguish between speech and noise using simple level detection techniques when parts of the speech utterance are buried below the noise.