Speaker identification system

Today, particular attention is paid and huge financial resources are allocated to speaker recognition in forensics and other applications banking technology, voice call centres, voice search, etc. In many cases, this is due to the use of audio recording devices to record crime and, particularly the widespread use of mobile technologies, as well as the use of various state-of-the-art technologies in the fight against crime and international terrorism. However, direct application of automatic speaker recognition ASR systems in forensics raises a number of issues. In general, ASR methods work well only under controlled conditions, sufficiently good signal quality and relatively long duration. There are currently several ASRs in the world that are intended for forensic use both in forensics as well as in criminal search and operational work.

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Content:

• Forensic Automatic Speaker Recognition (FASR) : Problems and prospects
• Speaker Identification Integrated Project (SIIP)
• Automatic Speaker Recognition for Authenticating Users in the Internet of Things
• Speaker recognition
• Adding Speaker Identification To Your Application
• We apologize for the inconvenience...
• Speaker Identification
• Speaker Verification and Identification
• Donate to arXiv

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Forensic Automatic Speaker Recognition (FASR) : Problems and prospects

Abstract For the communication, speech is one of the natural forms. A person's voice contains various parameters that convey information such as emotion, gender, attitude, health and identity. Speaker recognition technologies have wide application areas, The aim of this paper is to provide the some specific areas where Speaker Recognition techniques can be used.

Here we discuss three main areas where Speaker Recognition Technique can be used. They are authentication , surveillance and forensic speaker recognition. For the communication, speech is one of the natural forms. They are authentication, surveillance and forensic speaker recognition. Automatic speaker recognition, which is basically the intersection of two areas of computer science, first natural language technologies and the second one is biometrics.

Speech signal contains information related to human being such as linguistic information e. From the speech perception point of view, it also conveys information about the environment in which the speech was produced and transmitted [6].

The general area of speaker recognition is divided into three specific tasks. These are authentication, surveillance and forensic speaker recognition. Depending on the applications, the general area of speaker recognition again divided into three specific categories. The goal of the speaker identification task is to determine which speaker out of a group of known speakers produces the input voice sample. This process is called speaker detection. It is also considered as a true-or-false binary decision problem.

Speaker segmentation and clustering techniques are used in multiple-speaker scenarios. In many speech recognition and speaker recognition applications, it is often assumed that the speech from a particular individual is available for processing [4]. Automatic speaker recognition is example of a pattern recognition problem that finds some kind of patterns within some real-world sensor data.

For all problems of pattern recognition, a training phase and a testing phase is required. In training phase a user enrols by providing voice samples to the system. The system extracts speaker-specific information from the voice samples to build a voice model of the enrolling speaker. In the testing phase, a user provides a voice sample that is used by the system to measure the similarity of the user's voice to the model of the previously enrolled user and, subsequently, to make a decision.

For example, in speaker authentication system, valid users of the system need to be enrolled that will store voice sample in a database. Speech samples of the user are required for the training phase. During the later recognition process, the system compares another recorded speech signal to the training utterance. The expected output of the system can be the name of one of the training speakers, or a rejection of voice [5].

Speaker recognition could be categorized as speaker identification and speaker verification. Speaker verification could be either Text-dependent or Text-independent [5]. Text-dependent that is when the same text is spoken on both training and test phases. On the other hand, in Text-independent phase, there is no restriction of voice sample. It could be differ in training and test phases. In real life, Text-independent systems are more commercially attractive than Text-dependent systems because it is harder to mimic an unknown phrase than a known one.

Automatic speaker recognition use spectrum-related features based on very short time slices of speech. Speaker models based on such information suffer from a lack of robustness to channel mismatches, and fail to capture longer-range characteristics of how a person talks, including the speaker's word patterns, and patterns in speech prosody such as the timing, pausing, and intonation of speech [1]. Anatomical structure of the vocal tract is unique for every person and hence every person speech has different features.

That's why voice information available in the speech signal can be used to identify the speaker. Since differences in the. The selection of appropriate features along with methods to estimate extract or measure them is known as feature selection and feature extraction. After the features are extracted a test template is formed. In the classification the test and reference templates are compared and a measurement of similarity is calculated between the two templates.

If this measurement is within a threshold value, then the identity claim is accepted, otherwise it is rejected. Based on the threshold value, two types of results are possible, False acceptance and False rejections, In Speaker Recognition cases where voice verification is used to control access to secure buildings, False acceptances where a false claim is accepted might result in theft or fraud.

Therefore, in order to eliminate them the threshold values need to be high. But this might result in many false rejections where owner of the identity is rejected which are undesirable. On the other hand a low threshold value while not giving any false rejections could result in false acceptances.

In some classification methods two threshold values are used, that is low and high. If the measurement is below the low value then the claim is accepted and if it exceeds the high value then the claim is rejected.

If it is in between the two values then further classification is performed [5][6]. Speaker recognition technologies are used in wide application areas. In this research paper, we are discussing about three wide areas as well as related application areas where speaker recognition techniques can be used. These areas are authentication, surveillance and forensic speaker recognition [7].

Speaker recognition for authentication allows the users to identify person using their voices. A person can be identified by various characteristics like signature, fingerprints, voice, facial features etc. This type of authentication methods known as biometric person authentication. In this case, the chance of misused of these type of identity problems are lesser as compared to the key or credit card can be stolen or lost, followed by the PIN number or password can be easily misused or forgotten.

Each person has unique anatomy, physiology and learned habits that familiar persons use in everyday life to recognize the person. This can be much more convenient than traditional means of authentication which require to carry a key with you or remember a PIN [1][4].

Security agencies have several means of collecting information. One of these is electronic eavesdropping of telephone and radio conversations. As these results in high quantities of data, filter mechanisms must be applied in order to find the relevant information. One of these filters may be the recognition of target speakers that are of interest for the service [6]. It is an important application of speaker recognition. If there is a speech sample that was recorded during the crime.

The suspect's voice can be compared with this in order to give an indication of the similarity of the two voices. Proving the identity of a recorded voice can help to convict a criminal or. Although this task is probably not performed by a completely automatic speaker recognition system, signal processing techniques can be used in this field nevertheless [1][6].

The voice characteristics used in this system during experiments are as follows-. Speaker recognition could be used in credit card transactions as an authentication method combined with some others like face recognition.

Speaker recognition technology can provide transaction authentication facility or computer access control, monitoring, telephone voice authentication for long distance calling or banking access etc.

Speech and speaker recognition are dual research areas in the sense that speaker variability is one of the major problems in speech recognition, whereas in speaker recognition it is an advantage. Speaker recognition technology could be used to reduce the speaker variability in speech recognition systems by speaker adaptation. For instance, speech recognition system could have a speaker gating unit that recognizes who is speaking. Then, the system could adapt its speech recognizer parameters to suit better for the current speaker, or to select a speaker-dependent speech recognizer from its database [4].

In this several speakers are included in the audio recording. Also it is desired to know who is speaking in a teleconference especially when there are many attendants in the tele-conference and the attendants are not very familiar with each other. Three different type of multi-speaker tasks are recognized - speaker detection, speaker tracking, and speaker segmentation. The detection task consists of deciding whether a known speaker is present in a multi-speaker recording. In the tracking task, a given speaker's speaking intervals are located in the recording.

The segmentation task consists of locating the speech intervals of each different speaker. In the most general case, there might be no prior knowledge of the speakers or their number.

Applications of speaker segmentation have been proposed for segmentation of news broadcasts [4]. Such as voice-mail are becoming more and more popular due to the developments in speech technology in general. The above applications require robust speaker recognition techniques, e. In the meeting scenarios, participants may talk while moving around facing the microphone in different directions and different distances.

Mismatched conditions may be encountered at any time in these cases. Therefore robustness is one of the critical factors that decide the success of speaker recognition in these applications [1]. In this paper the aim is to provide information about various applications of Speaker Recognition Technologies. Automatic speaker recognition applications defines which information in the speech signal is relevant, such as the linguistic information will be relevant if the goal is to recognize the sequence of words that the speaker is producing.

The presence of irrelevant information like speaker or environment information may actually degrade the system accuracy. From the above discussion, if someone wants to see where Speaker Recognition can be useful, this paper will help in gathering information related to applications. Robust Speaker Recognition. Carnegie Mellon University Pittsburgh ISBN- , ISSN: December 21, Applications of Speaker Recognition Academic research paper on " Computer and information sciences ".

Abstract of research paper on Computer and information sciences, author of scientific article — Nilu Singh, R. Khan, Raj Shree Abstract For the communication, speech is one of the natural forms.

Speaker Identification Integrated Project (SIIP)

Open topic with navigation. IDOL Speech Server provides a set of speaker identification tasks that cover both the training of a set of speaker templates, and the identification of speakers by using this set. These sections cover the speaker identification training process in more detail, and give some basic guidance on how best to optimize the system and run speaker identification tasks. For more detailed information on optimization and speaker identification, and on the default speaker identification tasks, refer to the Speech Server Administration Guide. Calculate thresholds. Speech Server uses the score information in the ATD files to estimate a score threshold for each speaker template, and stores the value in the template file.

Automatic Speaker Recognition for Authenticating Users in the Internet of Things

Speaker recognition can be classified into either 1 speaker verification or 2 speaker identification Furui, ; J. Campbell, ; Bimbot et al. Speaker verification aims to verify whether an input speech corresponds to the claimed identity. Speaker verification is one case of biometric authentication, where users provide their biometric characteristics as passwords. Biometric characteristics can be obtained from deoxyribonucleic acid DNA , face shape, ear shape, fingerprint, gait pattern, hand-vein pattern, hand-and-finger geometry, iris scan, retinal scan, signature, voice, etc. Figure 2. Figure 3. High biometric characteristic scores on all above criteria except circumvention are preferable in real applications.

Speaker recognition

Abstract The main aim of this paper is speaker recognition. This can be achieved by automatically identify who is speaking on the basis of individual information integrated in speech waves. Objective is comparing a speech signal from a unknown speaker to database of known speaker. The system can recognize the speaker, which has been trained with a number of speakers.

Adding Speaker Identification To Your Application

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Speech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients MFCC have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network FFNN tuned by an optimized particle swarm optimization OPSO algorithm. A recognition accuracy of However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest RF , - nearest neighbour KNN , and support vector machine SVM. Voice is the oldest method of communication reported in human history on earth.

Speaker Identification

Bandung, ; 1: Kwon S, Narayanan S. Large-Scale Speaker Identification.

Speaker Verification and Identification

Abstract For the communication, speech is one of the natural forms. A person's voice contains various parameters that convey information such as emotion, gender, attitude, health and identity. Speaker recognition technologies have wide application areas, The aim of this paper is to provide the some specific areas where Speaker Recognition techniques can be used. Here we discuss three main areas where Speaker Recognition Technique can be used. They are authentication , surveillance and forensic speaker recognition. For the communication, speech is one of the natural forms.

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The ability of a system to recognize a person by their voice is a non-intrusive way to collect their biometric information. Moreover, in pandemic like situations where infectious diseases may exist on surfaces, voice recognition can easily be deployed in place of other biometric systems that involve some form of contact. The existing applications of person authentication include Speaker verification is a subfield within the broader Speaker recognition task. Speaker recognition is the process of recognizing a speaker by their voice.

Capture phrases in quotes for more specific queries e. Our world is becoming increasingly mobile. People rely on their mobile devices not only for communication, but for applications from commerce, to home automation and security, to banking, to entertainment, just to name a few. The challenge lies in how to remotely authenticate a person when there is no physical or visual contact.