Автор: Sadaoki Furui

Источник: http://www.scholarpedia.org/article/Speaker_recognition

Speaker recognition is the process of automatically recognizing who is speaking by using the speaker-specific information included in speech waves to verify identities being claimed by people accessing systems; that is, it enables access control of various services by voice (Furui, 1991, 1997, 2000). Applicable services include voice dialing, banking over a telephone network, telephone shopping, database access services, information and reservation services, voice mail, security control for confidential information, and remote access to computers. Another important application of speaker recognition technology is as a forensics tool.

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. These regression coefficients are respectively referred to as the delta-cepstral and delta-delta-cepstral coefficients.

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.

There is also a case called “open set” identification, in which a reference model for an unknown speaker may not exist. In this case, an additional decision alternative, “the unknown does not match any of the models”, is required. Verification can be considered a special case of the “open set” identification mode in which the known population size is one. In either verification or identification, an additional threshold test can be applied to determine whether the match is sufficiently close to accept the decision, or if not, to ask for a new trial.

The effectiveness of speaker verification systems can be evaluated by using the receiver operating characteristics (ROC) curve adopted from psychophysics. The ROC curve is obtained by assigning two probabilities, the probability of correct acceptance (false rejection rate) and the probability of incorrect acceptance (false acceptance rate), to the vertical and horizontal axes respectively, and varying the decision threshold. The detection error trade-off (DET) curve is also used, in which false rejection and false acceptance rates are assigned to the vertical and horizontal axes respectively. The error curve is usually plotted on a normal deviate scale.

The equal-error rate (EER) is a commonly accepted overall measure of system performance. It corresponds to the threshold at which the false acceptance rate is equal to the false rejection rate.

Speaker recognition methods can also be divided into text-dependent (fixed passwords) and text-independent (no specified passwords) methods. The former require the speaker to provide utterances of key words or sentences, the same text being used for both training and recognition, whereas the latter do not rely on a specific text being spoken. The text-dependent methods are usually based on template/model-sequence-matching techniques in which the time axes of an input speech sample and reference templates or reference models of the registered speakers are aligned, and the similarities between them are accumulated from the beginning to the end of the utterance. Since this method can directly exploit voice individuality associated with each phoneme or syllable, it generally achieves higher recognition performance than the text-independent method.

There are several applications, such as forensics and surveillance applications, in which predetermined key words cannot be used. Moreover, human beings can recognize speakers irrespective of the content of the utterance. Therefore, text-independent methods have attracted more attention. Another advantage of text-independent recognition is that it can be done sequentially, until a desired significance level is reached, without the annoyance of the speaker having to repeat key words again and again.

Both text-dependent and independent methods have a serious weakness. That is, these security systems can easily be circumvented, because someone can play back the recorded voice of a registered speaker uttering key words or sentences into the microphone and be accepted as the registered speaker. Another problem is that people often do not like text-dependent systems because they do not like to utter their identification number, such as their social security number, within the hearing of other people. To cope with these problems, some methods use a small set of words, such as digits as key words, and each user is prompted to utter a given sequence of key words which is randomly chosen every time the system is used. Yet even this method is not reliable enough, since it can be circumvented with advanced electronic recording equipment that can reproduce key words in a requested order. Therefore, a text-prompted speaker recognition method has been proposed in which password sentences are completely changed every time.

Text-dependent speaker recognition methods can be classified into DTW (dynamic time warping) or HMM (hidden Markov model) based methods.

In this approach, each utterance is represented by a sequence of feature vectors, generally, short-term spectral feature vectors, and the trial-to-trial timing variation of utterances of the same text is normalized by aligning the analyzed feature vector sequence of a test utterance to the template feature vector sequence using a DTW algorithm. The overall distance between the test utterance and the template is used for the recognition decision. When multiple templates are used to represent spectral variation, distances between the test utterance and the templates are averaged and then used to make the decision. The DTW approach has trouble modeling the statistical variation in spectral features.

An HMM can efficiently model the statistical variation in spectral features. Therefore, HMM-based methods have achieved significantly better recognition accuracies than DTW-based methods.