General Principles and Applications
Speaker identity is correlated with physiological and behavioral characteristics of the speech production system of an individual speaker. These characteristics derive from both the spectral envelope (vocal tract characteristics) and the supra-segmental features (voice source characteristics) of speech. The most commonly used short-term spectral measurements are cepstral coefficients and their regression coefficients. As for the regression coefficients, typically, the first- and second-order coefficients, that is, derivatives of the time functions of cepstral coefficients, are extracted at every frame period to represent spectral dynamics.
Speaker Identification and Verification
Speaker recognition can be classified into speaker identification and speaker verification. Speaker identification is the process of determining from which of the registered speakers a given utterance comes. Speaker verification is the process of accepting or rejecting the identity claimed by a speaker. Most of the applications in which voice is used to confirm the identity of a speaker are classified as speaker verification.
In the speaker identification task, a speech utterance from an unknown speaker is analyzed and compared with speech models of known speakers. The unknown speaker is identified as the speaker whose model best matches the input utterance. In speaker verification, an identity is claimed by an unknown speaker, and an utterance of this unknown speaker is compared with a model for the speaker whose identity is being claimed. If the match is good enough, that is, above a threshold, the identity claim is accepted. A high threshold makes it difficult for impostors to be accepted by the system, but with the risk of falsely rejecting valid users. Conversely, a low threshold enables valid users to be accepted consistently, but with the risk of accepting impostors. To set the threshold at the desired level of customer rejection (false rejection) and impostor acceptance (false acceptance), data showing distributions of customer and impostor scores are necessary.
The fundamental difference between identification and verification is the number of decision alternatives. In identification, the number of decision alternatives is equal to the size of the population, whereas in verification there are only two choices, acceptance or rejection, regardless of the population size. Therefore, speaker identification performance decreases as the size of the population increases, whereas speaker verification performance approaches a constant independent of the size of the population, unless the distribution of physical characteristics of speakers is extremely biased.