Why speaker recognition

Because the vocabulary is unlimited, prospective impostors cannot know in advance the sentence they will be prompted to say. This method not only accurately recognizes speakers, but can also reject an utterance whose text differs from the prompted text, even if it is uttered by a registered speaker.

Thus, a recorded and played back voice can be correctly rejected. This method uses speaker-specific phoneme models as basic acoustic units. One of the major issues in this method is how to properly create these speaker-specific phoneme models when using training utterances of a limited size.

The phoneme models are represented by Gaussian-mixture continuous HMMs or tied-mixture HMMs, and they are made by adapting speaker-independent phoneme models to each speaker's voice. In the recognition stage, the system concatenates the phoneme models of each registered speaker to create a sentence HMM, according to the prompted text. Then the likelihood of the input speech against the sentence model is calculated and used for speaker verification.

High-level features such as word idiolect, pronunciation, phone usage, prosody, etc. Typically, high-level-feature recognition systems produce a sequence of symbols from the acoustic signal and then perform recognition using the frequency and co-occurrence of symbols. The use of support vector machines for performing the speaker verification task based on phone and word sequences obtained using phone recognizers has been proposed.

The corpus was a combination of phases 2 and 3 of the Switchboard-2 corpora. Each training utterance in the corpus consisted of a conversation side that was nominally of length 5 minutes approximately 2.

Speaker models were trained using 1? These methods need utterances of at least several minutes long, much longer than those used in conventional speaker recognition methods. How can we normalize intra-speaker variation of likelihood similarity values in speaker verification?

The most significant factor affecting automatic speaker recognition performance is variation in signal characteristics from trial to trial inter-session variability, or variability over time. Speakers cannot repeat an utterance precisely the same way from trial to trial. It is well known that samples of the same utterance recorded in one session are much more highly correlated than tokens recorded in separate sessions.

There are also long term trends in voices. It is important for speaker recognition systems to accommodate these variations. Adaptation of the reference model as well as the verification threshold for each speaker is indispensable to maintaining a high recognition accuracy over a long period. In order to compensate for the variations, two types of normalization techniques have been tried? The latter technique uses the likelihood ratio or a posteriori probability.

This method is especially effective for text-dependent speaker recognition applications using sufficiently long utterances. In this method, cepstral coefficients are averaged over the duration of an entire utterance, and the averaged values are subtracted from the cepstral coefficients of each frame CMS; cepstral mean subtraction.

This method can compensate fairly well for additive variation in the log spectral domain. However, it unavoidably removes some text-dependent and speaker-specific features, so it is inappropriate for short utterances in speaker recognition applications.

It has also been shown that time derivatives of cepstral coefficients delta-cepstral coefficients are resistant to linear channel mismatches between training and testing. A normalization method for likelihood similarity or distance values that uses a likelihood ratio has been proposed. The likelihood ratio is the ratio of the conditional probability of the observed measurements of the utterance given the claimed identity is correct, to the conditional probability of the observed measurements given the speaker is an impostor normalization term.

Generally, a positive log-likelihood ratio indicates a valid claim, whereas a negative value indicates an imposter. This normalization method is, however, unrealistic because conditional probabilities must be calculated for all the reference speakers, which requires large computational cost. Another way of choosing the cohort speaker set is to use speakers who are typical of the general population. It was reported that a randomly selected, gender-balanced background speaker population outperformed a population near the claimed speaker.

A normalization method based on a posteriori probability has also been proposed. The difference between the normalization method based on the likelihood ratio and that based on a posteriori probability is whether or not the claimed speaker is included in the impostor speaker set for normalization; the cohort speaker set in the likelihood-ratio-based method does not include the claimed speaker, whereas the normalization term for the a posteriori-probability-based method is calculated by using a set of speakers including the claimed speaker.

Experimental results indicate that both normalization methods almost equally improve speaker separability and reduce the need for speaker-dependent or text-dependent thresholding, compared with scoring using only the model of the claimed speaker. A method in which the normalization term is approximated by the likelihood for a world model representing the population in general has also been proposed.

This method has an advantage in that the computational cost for calculating the normalization term is much smaller than the original method since it does not need to sum the likelihood values for cohort speakers.

A method based on tied-mixture HMMs in which the world model is made as a pooled mixture model representing the parameter distribution for all the registered speakers has been proposed. The use of a single background model for calculating the normalization term has become the predominate approach used in speaker verification systems.

Since these normalization methods neglect absolute deviation between the claimed speaker's model and the input speech, they cannot differentiate highly dissimilar speakers.

It has been reported that a multilayer network decision algorithm makes effective use of the relative and absolute scores obtained from the matching algorithm. A family of normalization techniques has been proposed, in which the scores are normalized by subtracting the mean and then dividing by standard deviation, both terms having been estimated from the pseudo imposter score distribution. Different possibilities are available for computing the imposter score distribution: Znorm, Hnorm, Tnorm, Htnorm, Cnorm and Dnorm Bimbot et al.

The state-of-the-art text-independent speaker verification techniques associate one or more parameterization level normalization approaches CMS, feature variance normalization, feature warping, etc. Since we cannot ask every user to utter many utterances across many different sessions in real situations, it is necessary to build each speaker model based on a small amount of data collected in a few sessions, and then the model must be updated using speech data collected when the system is used.

How to set the a priori decision threshold for speaker verification is another important issue. Since the threshold cannot be set a posteriori in real situations, we have to have practical ways to set the threshold before verification.

It must be set according to the relative importance of the two errors, which depends on the application. These two problems are intrinsically related each other. Methods for updating reference templates and the threshold in DTW-based speaker verification were proposed. The interspeaker distance distribution was approximated by a normal distribution, and the threshold was calculated by the linear combination of its mean value and standard deviation.

The intraspeaker distance distribution was not taken into account in the calculation, mainly because it is difficult to obtain stable estimates of the intraspeaker distance distribution from small numbers of training utterances. The reference template for each speaker was updated by averaging new utterances and the present template after time registration. These methods have been extended and applied to text-independent and text-prompted speaker verification using HMMs.

Then, we use this embedding extractor to compare the embeddings from one audio file coming from a single speaker with embeddings from an unknown speaker. For quantifying the similarity of the embeddings we use scoring techniques such as cosine similarity. Hands-on speaker recognition tutorial notebooks can be found under the speaker recognition tutorials folder. Documentation on dataset preprocessing can be found on the Datasets page. It also includes guidance for creating your own NeMo-compatible dataset, if you have your own data.

Text-independent verification has no restrictions on what the speaker says during enrollment, besides the initial activation phrase to activate the enrollment. It doesn't have any restrictions on the audio sample to be verified, as it only extracts voice features to score similarity. Speaker Identification enables you to attribute speech to individual speakers, and unlock value from scenarios with multiple speakers, such as:. Enrollment for speaker identification is text-independent , which means that there are no restrictions on what the speaker says in the audio, besides the initial activation phrase to activate the enrollment.

Similar to Speaker Verification, the speaker's voice is recorded in the enrollment phase, and the voice features are extracted to form a unique voice signature. In the identification phase, the input voice sample is compared to a specified list of enrolled voices up to 50 in each request.

Speaker enrollment data is stored in a secured system, including the speech audio for enrollment and the voice signature features. The speech audio for enrollment is only used when the algorithm is upgraded, and the features need to be extracted again. The service does not retain the speech recording or the extracted voice features that are sent to the service during the recognition phase. You control how long data should be retained. You can create, update, and delete enrollment data for individual speakers through API calls.

When the subscription is deleted, all the speaker enrollment data associated with the subscription will also be deleted. As with all of the Cognitive Services resources, developers who use the Speaker Recognition service must be aware of Microsoft's policies on customer data.

You should ensure that you have received the appropriate permissions from the users for Speaker Recognition. You can find more details in Data and privacy for Speaker Recognition. Feedback will be sent to Microsoft: By pressing the submit button, your feedback will be used to improve Microsoft products and services.

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