Editing Speaker recognition (section)

==Technology==
Speaker recognition is a [[pattern recognition]] problem. The various technologies used to process and store voice prints include [[frequency estimation]], [[hidden Markov model]]s, [[Gaussian mixture model]]s, [[pattern matching]] algorithms, [[Artificial neural network|neural networks]], [[matrix representation]], vector quantization and [[Decision tree learning|decision trees]]. For comparing utterances against voice prints, more basic methods like [[cosine similarity]] are traditionally used for their simplicity and performance. Some systems also use "anti-speaker" techniques such as [[cohort model]]s and world models. Spectral features are predominantly used in representing speaker characteristics.<ref>{{cite journal | last1=Sahidullah | first1=Md | last2=Kinnunen | first2=Tomi | title=Local spectral variability features for speaker verification | journal=Digital Signal Processing | publisher=Elsevier BV | volume=50 | year=2016 | issn=1051-2004 | doi=10.1016/j.dsp.2015.10.011 | pages=1–11 | bibcode=2016DSP....50....1S |url=https://erepo.uef.fi/bitstream/handle/123456789/4375/sahidullah_local_2016.pdf}}</ref> [[Linear predictive coding]] (LPC) is a [[speech coding]] method used in speaker recognition and [[speech verification]].{{Citation needed|date=October 2021}}

[[Ambient noise level]]s can impede both collections of the initial and subsequent voice samples. Noise reduction algorithms can be employed to improve accuracy, but incorrect application can have the opposite effect. Performance degradation can result from changes in behavioural attributes of the voice and from enrollment using one telephone and verification on another telephone. Integration with [[two-factor authentication]] products is expected to increase. Voice changes due to ageing may impact system performance over time. Some systems adapt the speaker models after each successful verification to capture such long-term changes in the voice, though there is debate regarding the overall security impact imposed by automated adaptation{{Citation needed|date=April 2021}}