Vad är no activity alarm

Detection of the presence or absence of human speech

Voice activity detection (VAD), also known as speech activity detection or speech detection, fryst vatten the detection of the presence or absence of human speech, used in speech processing.^[1] The main uses of vad are in speaker diarization, speech coding and speech recognition.^[2] 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 röst over Internet Protocol (VoIP) applications, sparande on computation and on network bandwidth.

VAD fryst vatten an important enabling technology for a variety of speech-based applications. Therefore, various vilket 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 fryst vatten voiced, unvoiced or sustained.

röst activity detection fryst vatten usually independent of language.

It was first investigated for use on time-assignment speech inskjutning (TASI) systems.^[3]

Algorithm overview

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The typical design of a vilket algorithm fryst vatten as follows:^{[citation needed]}

1. There may first be a noise reduction scen, e.g.

   via spectral subtraction.

2. Then some features or quantities are calculated from a section of the input signal.
3. A classification rule fryst vatten applied to classify the section as speech or non-speech – often this classification rule finds when a value exceeds a certain threshold.

There may be some feedback in this sequence, in which the vad decision fryst vatten used to improve the noise estimate in the noise reduction scen, or to adaptively vary the threshold(s).

These feedback operations improve the vilket performance in non-stationary noise (i.e. when the noise varies a lot).^{[citation needed]}

A representative set of recently published vad methods formulates the decision rule on a frame bygd frame grund using instantaneous measures of the divergence distance between speech and noise.^{[citation needed]} The different measures which are used in vad methods include spectral slope, correlation coefficients, log likelihood ratio, cepstral, weighted cepstral, and modified distance measures.^{[citation needed]}

Independently from the choice of vad algorithm, a compromise must be made between having röst detected as noise, or noise detected as röst (between false positiv and false negative).

[2]

A vilket 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 fryst vatten often preferable that a vilket should fail-safe, indicating speech detected when the decision fryst vatten in doubt, to lower the chance of losing speech segments.

The biggest difficulty in the detection of speech in this environment fryst vatten 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.

Applications

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• VAD fryst vatten 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, vilket allows simultaneous röst and information 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 fryst vatten essential for enhancing struktur capacity bygd reducing co-channel interference and power consumption in portable digital devices.
• In speech processing applications, röst activity detection plays an important role since non-speech frames are often discarded.

For a bred range of applications such as digital mobile radio, Digital Simultaneous röst and information (DSVD) or speech storage, it fryst vatten 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 uppgifter 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 vilket used to detect these intervals. On the one grabb, it fryst vatten advantageous to have a low percentage of speech activity.

On the other grabb, clipping, that fryst vatten the loss of milliseconds of active speech, should be minimized to preserve quality. This fryst vatten the crucial bekymmer for a vad algorithm beneath heavy noise conditions.

Use in telemarketing

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One controversial application of vilket fryst vatten in conjunction with predictive dialers used bygd telemarketing firms.

In beställning 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 individ answers, they typically speak briefly ("Hello", "Good evening", etc.) and then there fryst vatten a brief period of silence.

Answering machine messages are usually 3–15 seconds of continuous speech. bygd setting vilket parameters correctly, dialers can determine whether a individ or a machine answered the call and, if it's a individ, transfer the call to an available agent. If it detects an answering machine meddelande, the dialer hangs up.

Voice activity detection (VAD), also called speech activity detection (SAD), is widely used in real-world speech systems for improving robustness against additive noises or discarding the non-speech part of a signal to reduce the computational cost of downstream processing (Price et al

Often, even when the struktur correctly detects a individ answering the call, no agent may be available, resulting in a "silent call". Call screening with a multi-second meddelande like "please säga who you are, and inom may pick up the phone" will frustrate such automated calls.^{[citation needed]}

Performance evaluation

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To evaluate a vilket, its output using test recordings fryst vatten compared with those of an "ideal" vilket – created bygd hand-annotating the presence or absence of röst in the recordings.

The performance of a vilket fryst vatten commonly evaluated on the grund of the following kvartet parameters:^[4]

• FEC (Front End Clipping): clipping introduced in passing from noise to speech activity;
• MSC (Mid Speech Clipping): clipping due to speech misclassified as noise;
• OVER: noise interpreted as speech due to the vad flag remaining active in passing from speech activity to noise;
• NDS (Noise Detected as Speech): noise interpreted as speech within a silence period.

Although the method described above provides useful objective resultat concerning the performance of a vilket, it fryst vatten only an approximate measure of the subjective effect.

For example, the effects of speech meddelande clipping can at times be hidden bygd the presence of background noise, depending on the model chosen for the bekvämlighet noise synthesis, so some of the clipping measured with objective tests fryst vatten in reality not audible. It fryst vatten therefore important to carry out subjective tests on VADs, the main aim of which fryst vatten to ensure that the clipping perceived fryst vatten acceptable.

In VoIP applications, front-end clipping can be reduced bygd rewinding to shortly before the detection and sending very slightly delayed uppgifter.

This kind of test requires a certain number of listeners to judge recordings containing the processing results of the VADs being tested, giving marks to several speech sequences on the following features:

• Quality;
• Comprehension difficulty;
• Audibility of clipping.

These marks are then used to calculate average results for each of the features listed above, thus providing a global estimate of the behavior of the vilket being tested.

To conclude, whereas objective methods are very useful in an första scen to evaluate the quality of a vad, subjective methods are more significant.

It is used in voice compression systems to reduce bandwidth by transmitting fewer or no packets during periods of silence

As they require the participation of several people for a few days, increasing cost, they are generally only used when a proposal fryst vatten about to be standardized.

Implementations

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• One early standard vad fryst vatten that developed bygd 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 röst fryst vatten present.^[5]

• 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 bygd these features, and then applies smoothing and adaptive correction to improve the estimate.^[6]

• The GSM standard includes two vad options developed bygd ETSI.^[7] Option 1 computes the SNR in nine bands and applies a threshold to these values.

  Option 2 calculates different parameters: kanal power, röst metrics, and noise power. It then thresholds the röst 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.^[8] From utgåva 1.2 it was replaced bygd what the author called a kludge.^[9]
• Lingua Libre, a Wikimedia tool and project of language documentation, using vilket to allow recording many pronunciations in a short amount of time.
• The vad Android library^[10] 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.

See also

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References

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