Speaker Recognition System X64 (April-2022)

Speaker recognition is a tool to automatically recognizing who is speaking on the basis of individual information included in speech waves.
This technique makes it possible to use the speaker’s voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.
Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments).
The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients.
A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients. Therefore it provides a stabler representation from one repetition to another of a particular speaker’s utterances.
As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients.
Requirements:
￭ Matlab Signal Processing and Neural Net. Toolboxes

A Speaker Recognition System Product Key is a computer system which analyzes a speaker’s voice and authenticates the speaker’s identity. The speaker may be a user signing into an account. If authentication is successful, the speaker can access a selected service.
Most speaker recognition systems include four distinct stages: Voice sample collection—The input speaker generates a voice sample. Feature extraction—The speaker sample is converted into a feature vector to be examined. Feature analysis—The feature vector is tested against a speaker recognition model to determine speaker identity. Speaker authentication—A verification strategy is employed to indicate whether or not the input speaker is a valid user.
The first three stages can be executed either offline or online. The voice sample is usually a short audio clip, the duration of which is less than 3 seconds and sampled at 16 KHz.
After the feature extraction stage, the speaker’s voice is first analysed as raw signal. Typically, the spectrum of the raw signal is first converted to cepstral coefficients. The cepstral coefficients are digitally sampled at some constant interval. For example, a 32-point analysis window with a sampling frequency of 16 KHz produces a 20000 point sample vector from each 25 msec-long sample.
Different basis sets of cepstral coefficients are used in different speaker recognition systems to represent the spectrum of a speech sound. The two most common cepstral coefficient sets are Mel-frequency cepstral coefficients and Linear Predictive Coding (LPC). Mel-frequency cepstral coefficients are the most popular and have been widely used. However, they do not convey the temporal information of a speech sound sufficiently.
Linear Predictive Coding (LPC) can represent both the spectral and temporal properties of a speech sound. LPC is derived from linear prediction of the spectral amplitude at a given frame period. Linear prediction coefficients are used to predict the next frame from the preceding frame. Therefore, LPCs do not directly represent the speech spectrum. LPCs take the frame period as the basic unit to represent a spectrum. Linear prediction of the frame period can predict the spectral amplitude for a number of frames to come.
LPC coefficients are spectral envelope model coefficients representing the time-varying spectrum with a linear combination of a finite set of basis functions.
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The speaker recognition system can be thought as divided into four main sub-systems, each of them needs to be designed in a way such that the final system has the required robustness with high accuracy.
Speaker recognition systems generally comprises: A feature extraction part: from the spectrum recorded by the microphone, a representation of the speaker’s voice is established by comparing it with a template. The template used is the speaker’s voice in the public spaces. It is generally called the training template, and it corresponds to the system user’s voice. A comparison and matching part: it compares the feature vectors extracted by the feature extraction part with the user’s voice in the public spaces of the system and returns the identity of the user. It corresponds to the recognition part of the system. A verification part: it takes as input the identity returned by the recognition part, and it checks whether the user’s voice is the same as the one of the user recorded by the system. A security part: it checks if the identity of the user matches the identity of the user designated for the particular speaker recognition system.
Each of these sub-systems must be robust enough to perform well in many circumstances, and its operation must be transparent to the user.
The first sub-system is the feature extraction part. The speech is a time varying signal which is characterized by the spectral and supra-segmental components. The spectral component is described by a set of frequency coefficients sampled at a given rate. The supra-segmental component is the set of time functions of the spectral coefficients.
Each sub-system is robust when used in its own way, but this is not necessarily the case when these three sub-systems are joined in a cascade.
This is because the co-adaptation between the feature extraction part and the following sub-systems is lost in this configuration.
The Features Extraction Part: the Training Template
A speaker recognition system for personal identification of a given speaker, from a voice sample that is collected by the system, consists of two main parts: A feature extraction part which computes the feature vectors representing the speech sample; and A recognition part which compares these feature vectors with the feature vectors
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A speaker recognition system must meet several requirements: high accuracy, high efficiency, and high cost-effectiveness. Most applications and vendors have either requirements, which meet only one of these criteria, or a compromise between them.
Accuracy and efficiency in a speaker recognition system may be summarized as: high probability, small error rate. They are achieved in different ways.
Accuracy is achieved by:
1- Short term spectral measurements: there are several techniques, such as Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC). They are used because they are simple to process. They have less training phases (0 or 1), and the number of coefficients used (around 160).
2- Long term spectral measurements: they perform better in robustness, by analyzing a long speech segment, and they can be used for speaker verification, which is a key factor in authentication.
Analysis:
MFCC: it corresponds to a system which uses a couple of Mel-frequency cepstral coefficients computed at every frame period (of frequency 0.5 kHz). The goal of MFCC is to compensate for the change of the laryngeal area as the speaker changes their pitch.
Usually a 11th-order LPC filter is used to extract the MFCCs. At a given frame period, the first- and second-order MFCCs are used to represent the short-term spectral characteristics. On the other hand, the first- and second-order LPC coefficients are used as a representation of the long-term spectral envelope dynamics.
In a short-term spectral envelope analysis, it is well known that the first- and second-order cepstral coefficients and their regression coefficients (first- and second-order delta- and delta-delta-cepstral coefficients) are sufficient to obtain an accurate representation of the spectral envelope at the frame period.
In a long-term analysis, the use of cepstral coefficients less than the first-order coefficients is necessary. In most cases, the use of a fourth-order filter instead of a second-order filter is enough.
Cost-effective speaker recognition is difficult to achieve if we use a system that has to be trained, which takes a lot of time and needs a large number of long training examples.
The goal of a speaker recognition system is to train as many training samples as possible. The system then has to be able to classify the trained training samples in the smallest number

Speaker Recognition Systems can be classified in different ways, depending on the speech content. They can be differentiated according to the type of model that is used or on whether the system is spectral or sub-band based.
The output of the system can be: The identity of the speaker (positive match) or No identification of the speaker (negative match) Some information about the speaker (confirmation)
The algorithm of a typical speaker recognition system is shown in FIG. 1.
Two forms of speaker recognition are used: speaker identification and speaker verification.
Speaker recognition algorithms use the well-known front-end processing (PRE) and back-end processing (PEP) in addition to both segmental and supra-segmental features.
The system used for speaker recognition identifies a speaker by comparing the utterance heard by the detector to the speech templates stored in a reference database. The templates represent the voice, so they must be created by a speaker of the same gender and with a similar speech repertoire.
The speaker recognition system includes a front-end processing module, a template database and a back-end processing module.
There are four basic steps used in speaker recognition systems:
The Front-End
The Front-End module is responsible for extracting the speaker’s information from the speech signals. It consists of a front-end processor which is responsible for the segmental feature extraction, and an entropy cepstral vector processor which is responsible for the feature extraction from the supra-segmental information.
The Front-End Module
The Front-End module includes a feature extraction module that is responsible for the extraction of the relevant features that describe the speech signal and it is comprised of two independent feature extraction modules: a segmental feature extraction module and a supra-segmental feature extraction module.
Segmental Features (Cepstral Features):
The cepstral features extract the spectral envelope, which is a representation of the acoustic characteristics of the speech signal. These features extract information about the short-time spectrum of the signal that is commonly used in speech coding systems. This information is extracted by analyzing the linear predictive coding (LPC) parameters of the signal. The LPC parameters are estimated by passing the speech signal through an LPC analysis filter, which is a low-pass filter with a time-

– Minimum: OS: Windows 7
– Minimum: Ram: 1 GB
– Minimum: Graphics: AMD Radeon HD 6970
– Minimum: Resolution: 1024×768
– Minimum: DirectX: 11
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