Voice Biometric Authentication Techniques for Banking Security

Contactless authentication using voice recognition to improve security, convenience, and cross-platform compatibility compared to conventional authentication methods that require contact. The authentication is performed by converting an audio sample from a user device into a processed format and transmitting it to a speech recognition module. The speech recognition module compares the processed audio against user voice models to determine if it matches. If the match is successful, the user is authenticated. This allows contactless voice-based authentication that can be used on any device with a microphone and speaker, without requiring dedicated authentication devices or manual entry.

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Secure electronic financial transactions system that uses AI, biometrics, and multimodal data analysis to prevent fraud and improve user experience. The system collects multimodal data from visual, auditory, and tactile sensors during transactions. It uses AI modules like biometric authentication, transaction anomaly detection, geospatial analysis, behavioral analysis, and third-party data integration to analyze this data for fraud prevention. The system also communicates with users through modalities like vision, audio, touch, taste, smell, temperature, pain, and balance to address fraud concerns or request verification.

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Contactless authentication system using voice recognition that allows user devices to authenticate without physical contact or dedicated authentication devices. It involves receiving user voice data, converting it to a standard format, sending it to a remote server for voice matching, and using the match result to authenticate the user. This allows cross-platform compatibility since the authentication is performed remotely and doesn't require device-specific formats.

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Quickly realizing highly secure voice authentication without requiring learning a global voiceprint model, using a calculation method like the Viterbi algorithm, or calculating an average reliability value. The method involves extracting a voice feature vector from a user's registered voice, generating a temporary password, presenting it to the user, receiving their voice reading the password, and authenticating using both voice feature vectors and the password. This allows fast voice authentication without complex speech recognition processing.

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Privacy-protecting voiceprint authentication to prevent exposure of user biometric data in voice authentication systems. The method involves synthesizing a voiceprint template by combining extracted voice features from a user's voice spoken in multiple modes, rather than sending the actual voice. This synthesized template is used for authentication instead of the original voice. This prevents exposing the user's actual voice during registration and authentication. The synthesized voiceprint template is sent to the server for storage, which cannot be reversed to obtain the user's original voice.

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Enhancing user authentication using a smart device like a smart speaker to improve security and reliability. The method involves using transaction-based authentication questions like "What was your credit card purchase on May 12?" instead of static questions. The user's voice is analyzed during the responses to determine if it matches an expected voiceprint. If so, fewer questions are needed for authentication. If not, more questions are required. This combines voice analysis with dynamic transaction-based questions to prevent spoofing of prerecorded voices.

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Voice verification method and network equipment that enhances the accuracy and security of voice password verification while also providing real-time performance. The method involves using a voice recognition server to compare a user's live voice input against a stored voice print, instead of using pre-recorded voice files. The live comparison reduces the risk of stolen usernames and forgotten passwords. The voice recognition server extracts speech features from the live input and compares them against the stored voice print in real-time. This avoids issues with voice details changing due to emotion or state differences between password setting and verification.

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Multi-factor authentication system that includes voice verification as a factor. The system allows users to enroll their voice for authentication by providing a few sample utterances. It trains a text-independent voice model using the samples to capture distinctive aural characteristics of the user's voice. During authentication, the system compares the user's live voice to the trained model to verify their identity. This provides an additional factor of authentication beyond passwords that is more convenient than using hardware tokens or biometrics.

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Exchanging data for secure transactions over voice channels to enable voice-based payment and other transactions. The method involves monitoring voice conversations for transaction requests, extracting details from the voice, confirming with the user, and carrying out the transaction using the extracted data. It uses voice encoding to embed transaction requests in audio and voice decoding to extract them. This allows performing transactions without separate channels or devices.

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Continuously authenticating a person by voice to extract their voice from a mixed audio stream or continuously monitor if an ongoing call is with a specific person. The technique involves continuously performing voiceprint authentication on the acquired voice in time series using stored voiceprint data. Authentication result values representing estimation degrees are generated. Thresholds are used to selectively pass or discard the voice based on how closely it matches the target. This allows extracting just the target person's voice from a mixed stream or continuously monitoring if a call is with the target.

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Enhanced security for accessing resources like bank accounts using biometric verification at the time of transaction in addition to PIN entry. The method involves comparing current biometric data with historical data to ensure consistency. This prevents unauthorized use of stolen cards or compromised PINs. A generative adversarial network (GAN) is trained to enhance biometric data and generate distributions for verification. It analyzes discrepancies and determines if multiple users could match the data.

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Enabling voice biometric authentication for user accounts that allows non-intrusive collection of voice data during normal calls to enroll users and authenticate them without disrupting the call flow. The system enhances audio quality during calls to capture voice samples for enrollment. It filters background noise to focus on high-definition voice characteristics. This minimally intrusive enrollment is performed during normal conversations between users and agents. The enhanced audio is stored and converted to voiceprints for authentication.

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Authenticating user identity in digital services and voice calls using voice analysis instead of passwords or security questions. The method involves comparing a user's recorded voice speaking a unique identifier against their stored voice record to authenticate. Speech recognition extracts the identifier, and voice comparison verifies identity. It enables hands-free login and call access without manual entry or remembering passwords.

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Training a machine learning model for simultaneously authenticating a user and determining liveness of the user's biometric voice data. The training involves using a dataset of audio signals with user identities and flags indicating if the audio was live or synthetic. The model learns embeddings from the audio signals and optimizes losses like triplet loss to successfully verify user identity and liveness. This joint training allows the model to authenticate users and detect spoofing attacks like voice cloning or conversion.

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A terminal device that allows user authentication using multiple modalities to reduce burden and improve success rates. The device can authenticate using face recognition or voice recognition. If face authentication fails, it switches to voice authentication using vocal tract characteristics. This allows fallback authentication if one modality fails.

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Enhancing online account security by adding voice biometric authentication to the two-factor authentication process. When a user tries to log in to an online account, in addition to entering a password, they are prompted to provide a voice sample. The system compares the recorded voice to a known voice biometric profile for that user. If the match is successful, the user is granted access to the account. This helps prevent unauthorized access using stolen passwords or compromised SIM cards, as it requires the user's live voice to authenticate.

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Authenticating users via voice prints in self-service systems to prevent voice phishing attacks. The method involves generating a text-independent voice print for user enrollment. When a user requests authentication, a passphrase is selected based on comparing it against text-dependent voice biometric models. The user is asked to repeat the selected passphrase. Their recorded response is compared against the text-independent voice print to authenticate them. This prevents phishing attacks as the passphrase is unique per session.

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Securely validating credentials like bank account numbers without exposing the actual credentials. The method involves training a neural network to infer validity of encrypted credentials. The neural network is then encrypted using the same homomorphic encryption as the credentials. This allows validating encrypted credentials without decrypting them. The encrypted validity indicators can be provided back to the original source without exposing the original encrypted credentials.

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Self-service correction terminal anti-counterfeiting identity verification method that uses dynamic speech with stop commands and image cues to prevent forgery. The method involves generating anti-counterfeiting rules with dynamic sentences and stop commands for voice authentication. These rules are displayed to users. They read the sentences with stops, and the terminal collects the voice. It extracts voiceprint features, recognizes speech segments, and compares against the dynamic rules. This makes it harder to forge voices since stops and images are required.

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Mobile banking application security authentication using voice recognition instead of passwords or biometrics. The method involves comparing voice recordings to stored voiceprints to authenticate users. When a user requests a banking action, the app plays a prompt and records their voice. It compares the recorded voice to the user's stored voiceprint to verify identity. If no voiceprint exists, it prompts the user to record one. This provides convenience without requiring active participation like fingerprints or passwords.

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Improving user identification accuracy by extracting user features from their voice to verify their identity. When a user requests verification, their voice input is analyzed to extract features like biometric info, voice characteristics, and semantic content. These features are then used to determine if the user is trusted and authorized. This provides more accurate user identification compared to manual verification by agents.

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Authenticating users based on their behavioral biometric data like speech patterns and touch inputs to improve accuracy and prevent impersonation compared to traditional authentication methods like PINs or passwords. The technique involves generating challenge questions based on the user's recent transactions, prompting the user to respond audibly and/or via touch, extracting behavioral biometric data from the responses, and using machine learning models trained on user-specific biometric data to verify the user's identity.

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Adversarially robust voice biometrics, secure recognition, and identification to authenticate and verify user voices in a way that can detect and prevent fraudulent attempts to impersonate users. The technique involves analyzing voice characteristics to calculate a voice resemblance score between the user's voice print and the presented voice. If the score exceeds a threshold, indicating a close match, it indicates the presented voice is likely the user's. This provides robust voice authentication that can detect and reject replays of recorded user voices or synthetic voices created to impersonate.

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Electronic bank identity authentication using voiceprint, content, and emotion recognition to prevent spoofing and improve security without passwords. The method involves extracting features from user voice, passing through a trained neural network, and comparing against voiceprint, content, and emotion templates. If all comparisons exceed thresholds, authentication succeeds. This combined voice recognition prevents spoofing by verifying unique voiceprint, content, and emotion patterns.

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Spoofing-proof voice authentication for devices that combines text-dependent and text-independent voice recognition with anti-spoofing techniques to prevent unauthorized access. The method involves extracting features from a user's wake-up phrase and command, feeding them to pre-trained neural networks, matching against text models, computing scores, and comparing against thresholds. Anti-spoofing involves extracting features like FFT/DCT, feeding to neural nets, computing confidence scores, and comparing against thresholds. The final authentication score is a weighted sum of the matching and anti-spoofing scores.

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Authorizing transactions involving smartcards by combining smartcard usage with biometric authentication. The method involves receiving a policy message with ruleset for transaction authorization and biometric parameters. Biometric data is received and an authentication score calculated using the machine learning algorithm. The transaction is checked against the ruleset to authorize. This combines smartcard usage with biometric verification to provide improved security and reduce false positives in transaction authorization.

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High-speed voice recognition method for secure user authentication in smart POS machines that eliminates the need for external devices and reduces complexity compared to fingerprint or digital certificate authentication. The method involves using voice recognition as the authentication factor. The smart POS machine securely stores the user's private key in a trusted storage block. The user's voice input is compared against a stored voice model using a blackman distance metric. If the voice match passes, the smart POS machine can log in using the stored private key. This eliminates the need for transmitting identification data or exporting cryptographic devices.

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Authenticating payment transactions using behavioral biometrics to quickly determine user identity and approve transactions without multiple authentication steps. The method involves embedding a behavioral biometrics service in merchant webpages/apps that collects transaction and user data. This data is sent to a behavioral biometrics server for analysis to generate an authentication response. If the response indicates lack of authenticity, an alert is sent to merchant, app, and issuer. This allows quicker approval without extra authentication if behavior matches known patterns. It also provides an additional layer of authentication called cloaking for PSD2 compliance.

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Personal identification system using homomorphic encryption of voices to provide secure and fast user authentication without exposing biometric data. The system involves encrypting user voices homomorphically, transmitting the encrypted voices to a server, and calculating encrypted identification results based on the encrypted voices. The server returns the encrypted results to the devices, which decrypt them to identify users. The homomorphic encryption allows calculating results on encrypted data without decrypting. This protects user voices while enabling identification without exposing biometrics.

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Multi-factor authentication using voice for secure user authentication. The method involves generating a voice challenge from an authentication server, sending it to the user, and having the user respond with their voice. The server compares the user's voice to the stored voiceprint to authenticate. The challenge has a validity period to prevent replay attacks. This allows convenient voice-based authentication for scenarios like account opening, password reset, and 2FA without requiring additional devices.

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A security tool that uses a user's location history and voice biometrics to detect and prevent credit card fraud. The tool analyzes historical transactions, locations, and voice data to determine fraud probability. If a transaction looks suspicious, it requires vocal confirmation from the user. If the voice matches, the transaction is authorized. This reduces false positives compared to just transaction history. It also allows faster reactivation of frozen accounts.

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Reducing the risk of unauthorized operation on an information processing device after user authentication by adding a secondary voice authentication step. After initial authentication using a different method than voice comparison, the device compares the user's voice during operation to the registered voice to confirm it's the same user. This prevents impersonation attempts after initial authentication. The secondary voice comparison uses the known user's voice features instead of comparing against all voices, reducing processing load compared to full voice recognition.

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Multi-step voice authentication for securely capturing and transmitting user operation sequences using voice recognition. The method involves two levels of voice authentication: first, to authenticate the user's voice, and second, to confirm the voice matches the specific operation being performed. This second step ensures the voice is indeed the user's for that operation. If the second authentication fails, the operation sequence is sent elsewhere. This prevents spoofing by replaying pre-recorded voices for operations. The second authentication can be based on voice characteristics, length, etc.

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Biometric authentication system that prevents voice biometric spoofing attacks by providing end-to-end cryptographic protection of the audio data from the headset device to the biometrics module. The headset encrypts/signs the user's audio before sending it to the host device. This ensures the authenticity of the audio received at the biometrics module, preventing malicious modification or substitution of the audio during transmission.

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User authentication using voice input that reduces the user burden compared to memorizing and accurately pronouncing a registered voice. Instead, the system generates voice passwords based on registered voice features. During registration, the user's voice is recorded and features extracted. If not matching any existing user's features, a new voice password is generated from the registration voice features. In authentication, the system presents a randomly selected voice password for the user to speak. If it matches, authentication succeeds. This allows users to authenticate without memorizing exact voices, as the system generates unique voice passwords based on registered voices.

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A secure user authentication and registration method that uses voice processing to extract unique feature identifiers instead of storing direct personal information. The method involves transforming a user's voice through an algorithm to get a voice feature map. This map is then fed through a neural network to extract deep features. The feature identifier associated with these deep features is used to represent the user's identity. During authentication, the feature identifier is checked against stored feature-blockchain key pairs. This provides higher security than direct storage of personal data like fingerprints or faces.

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A voice authentication method for electronic devices that uses interactive voice commands to enhance authentication speed and accuracy. The method involves acquiring user identification and voice print information from a voice command, searching reference voice print for that user, and authenticating based on the acquired and reference voice prints. If matching ratio is below thresholds, it requests an additional speech or identifies another user. This interactive authentication provides better accuracy than static voice passwords.

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Dynamic voice authentication system that matches a person's current utterance with previously recorded ones to authenticate them. It isolates words and acoustic characteristics from utterances, compares them with stored ones, and transmits an authentication message if matches. This improves over static phrase authentication as it can handle pronunciation variations and compromised phrases. The system uses statistical and acoustic analysis to extract characteristics, and allows speaker repetition of generated phrases to further improve matching.

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Determining whether voice biometrics is a reliable authentication method for a user by considering factors beyond just the voice biometric data. The method involves obtaining user-specific information like business rules and prompts the user for additional info. It then determines if the voice biometrics credential is usable based on the voice data, user info, and rules. This provides a more comprehensive assessment of voice biometrics reliability compared to just the voice alone.

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Method for authenticating users using their voice commands in voice interactions without requiring additional authentication steps. The method involves matching voiceprint features of the user's voice commands against a stored standard voiceprint to authenticate the user. By verifying user identity through voice commands, it allows seamless and uninterrupted authentication during voice interactions. The matching threshold can be adjusted to adapt to user voice changes.

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A method for improving the security of user authentication using voice and gestures to prevent voice impersonation attacks. The method involves collecting user identification information containing voice and action data during enrollment. During authentication, a randomly generated text prompt is provided and the user's voice and gesture are recorded. The recorded data is compared against the preset data. If the voice and gesture match, the authentication succeeds, but if either fails, it fails. This adds a second factor of gesture recognition to prevent voice impersonation.

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Authenticating users in voice-based systems to improve security by using multifactor authentication (MFA) techniques that involve sending a one-time password (OTP) to a user's first device, having them speak it to a second device, and then transmitting the spoken version to the server. The server compares it to the original OTP and if they match, authenticates the user. This allows voice authentication without compromising security since the spoken OTP is only transmitted to the server and not directly over the network.

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A secure and convenient method for online financial transactions using voice verification on mobile phones. The system allows users to make transactions up to INR 50,000 using voice authentication instead of typing card details. It combines voice verification, password verification, and location verification. Users enroll by speaking a voice segment and answering a unique question. For future transactions, they speak the segment again and it's compared to the enrolled voice. This prevents fraud from recorded voices.

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Bank identity recognition system using voice recognition to simplify authentication over phone calls. The system collects user voiceprints and allows authorized access to decrypt and extract them for comparison. This enables voiceprint-based authentication without needing physical biometrics. A user's identity is initially verified via other means like face, graphics, passwords, or iris scans before allowing voiceprint access. This prevents unauthorized voiceprint retrieval.

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Hardware and software systems, devices, networks, and methods for identity recognition and verification based on voice spectrum analysis that provides increased levels of security that do not necessarily rely on all user data being secure, and are more convenient for the user. The system uses voice biometrics to verify identity by analyzing the spectrum of voice samples instead of relying solely on the acoustic characteristics. It extracts sub-spectra from voice samples to compare harmonic patterns between formants. This allows identifying biological relationships between speakers and detecting spoofing attempts using recorded samples. The system also enables text-independent verification, as it focuses on the spectrum rather than speech content.

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Secure authentication system for audio interface devices like smart speakers that allows remote voice commands while mitigating fraud risks. The system generates audio transmissions containing encoded one-time passwords along with secondary artifacts. These transmissions are sent to verified user devices associated with accounts. When a request comes from an audio interface, the system compares the received audio to the stored reference. If it matches, the transaction is authorized. This verifies the user without requiring them to speak passwords aloud, addressing security concerns of using voice commands for payments.

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Voice registration and authentication using a voice ID system that improves speech recognition accuracy compared to traditional methods. The system generates a unique voice identifier for each user based on their recorded voice. This identifier is then used to authenticate subsequent voice inputs. The identifier is extracted by training an integrated voice model and scoring model on reference voices. By quantifying similarity between voices instead of directly recognizing speech, it addresses limitations of speech recognition like dialects and new terms.

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Identity verification method for improving user experience and security when users cannot easily enter verification codes. It adapts the verification process based on user characteristics. Users who struggle with keyboard entry can authenticate using voice recognition instead. Users with language barriers can choose face recognition, fingerprint, or keyboard input methods. This avoids inconvenience and security issues for users who can't easily use traditional verification codes.

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User authentication method using extracted audio features to improve security and convenience compared to traditional methods like SMS and voice calls. The method involves storing initial authentication data that includes both audible user voice and inaudible environmental sound when enrolling. During authentication, the user's voice and environment sound are extracted and compared against the initial data to verify authenticity. This uses unique environmental characteristics to enhance security and prevent spoofing. The user can also transmit a predetermined inaudible frequency along with their voice for authentication.

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Improved voice-based user authentication using multi-dimensional feature extraction. The method involves obtaining a user's voice signal, generating feature data with multiple dimensions from the voice, and determining user identity based on the multidimensional features. This provides better accuracy and security compared to single frequency-based authentication since it captures and analyzes different aspects of the voice signal.

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