Обнаружение и анализ речевой активности

Detection of the presence or absence of human speech
Voice activity detection (VAD), also known as speech activity detection or speech detection, is the detection of the presence or absence of human speech, used in speech processing. The main uses of VAD are in speaker diarization, speech coding and speech recognition. It can facilitate speech processing, and can also be used to deactivate some processes during non speech section of an audio session: it can avoid unnecessary coding/transmission of silence packets in Voice over Internet Protocol (VoIP) applications, saving on computation and on network bandwidth. VAD is an important enabling technology for a variety of speech based applications. Therefore, various VAD algorithms have been developed that provide varying features and compromises between latency, sensitivity, accuracy and computational cost. Some VAD algorithms also provide further analysis, for example whether the speech is voiced, unvoiced or sustained. Voice activity detection is usually independent of language. It was first investigated for use on time assignment speech interpolation (TASI) systems.

VAD is an integral part of different speech communication systems such as audio conferencing, echo cancellation, speech recognition, speech encoding, speaker recognition and hands free telephony. In the field of multimedia applications, VAD allows simultaneous voice and data applications. Similarly, in Universal Mobile Telecommunications Systems (UMTS), it controls and reduces the average bit rate and enhances overall coding quality of speech. In cellular radio systems (for instance GSM and CDMA systems) based on Discontinuous Transmission (DTX) mode, VAD is essential for enhancing system capacity by reducing co channel interference and power consumption in portable digital devices. In speech processing applications, voice activity detection plays an important role since non speech frames are often discarded. For a wide range of applications such as digital mobile radio, Digital Simultaneous Voice and Data (DSVD) or speech storage, it is desirable to provide a discontinuous transmission of speech coding parameters. Advantages can include lower average power consumption in mobile handsets, higher average bit rate for simultaneous services like data transmission, or a higher capacity on storage chips. However, the improvement depends mainly on the percentage of pauses during speech and the reliability of the VAD used to detect these intervals. On the one hand, it is advantageous to have a low percentage of speech activity. On the other hand, clipping, that is the loss of milliseconds of active speech, should be minimized to preserve quality. This is the crucial problem for a VAD algorithm under heavy noise conditions.

One controversial application of VAD is in conjunction with predictive dialers used by telemarketing firms. In order to maximize agent productivity, telemarketing firms set up predictive dialers to call more numbers than they have agents available, knowing most calls will end up in either "Ring – No Answer" or answering machines. When a person answers, they typically speak briefly ("Hello", "Good evening", etc.) and then there is a brief period of silence. Answering machine messages are usually 3–15 seconds of continuous speech. By setting VAD parameters correctly, dialers can determine whether a person or a machine answered the call and, if it's a person, transfer the call to an available agent. If it detects an answering machine message, the dialer hangs up. Often, even when the system correctly detects a person answering the call, no agent may be available, resulting in a "silent call". Call screening with a multi second message like "please say who you are, and I may pick up the phone" will frustrate such automated calls.

One early standard VAD is that developed by British Telecom for use in the Pan European digital cellular mobile telephone service in 1991. It uses inverse filtering trained on non speech segments to filter out background noise, so that it can then more reliably use a simple power threshold to decide if a voice is present. The G.729 standard calculates the following features for its VAD: line spectral frequencies, full band energy, low band energy (<1 kHz), and zero crossing rate. It applies a simple classification using a fixed decision boundary in the space defined by these features, and then applies smoothing and adaptive correction to improve the estimate. The GSM standard includes two VAD options developed by ETSI. Option 1 computes the SNR in nine bands and applies a threshold to these values. Option 2 calculates different parameters: channel power, voice metrics, and noise power. It then thresholds the voice metrics using a threshold that varies according to the estimated SNR. The Speex audio compression library uses a procedure named Improved Minima Controlled Recursive Averaging, which uses a smoothed representation of spectral power and then looks at the minima of a smoothed periodogram. From version 1.2 it was replaced by what the author called a kludge. Lingua Libre, a Wikimedia tool and project of language documentation, using VAD to allow recording many pronunciations in a short amount of time. The VAD Android library utilizes a combination of GMM and DNN models, such as WebRTC GMM, Silero DNN, and Yamnet DNN. The library surpasses many production grade models in both quality and performance.