Verifying Magic Ben

One of the areas where blockchain has shown immense potential is identity verification. Our project is an on-chain speaker verification neural network model that aims to revolutionize identity verification by leveraging the unique characteristics of individuals' voices. Deployed on the Cartesi platform, this project represents a powerful combination of blockchain's transparency and decentralization with the sophisticated capabilities of neural networks for voice recognition.

Traditional methods of identity verification, such as passwords or fingerprint scans, are susceptible to security breaches and identity theft. Biometric technologies, including voice recognition, offer an innovative and more robust approach to identity verification.

Voice biometrics, a form of speaker verification, analyzes an individual's unique vocal characteristics, including pitch, tone, and speech patterns, to create a voiceprint. Just like fingerprints, voiceprints are distinct to each person, making it highly reliable for identity verification. This technology holds tremendous promise in enhancing security measures, ensuring access to authorized individuals while thwarting unauthorized access attempts.

By leveraging Cartesi, our project can overcome the computational limitations of traditional on-chain smart contracts, enabling the deployment of sophisticated machine learning models such as neural networks. By using Kaldi I vector models, we are able to achieve computation efficiency and accuracy for the verification process.

The project's potential use cases are diverse and far-reaching. In the financial sector, speaker verification can be employed for secure access to banking accounts, preventing fraudulent transactions, and enhancing user authentication for financial services. In the healthcare industry, it can be used to secure access to sensitive patient information and ensure the accuracy of medical records.

Our on-chain speaker verification neural network model, deployed on the Cartesi platform, represents a pioneering approach to identity verification. By leveraging the unique characteristics of individuals' voices, we offer a robust and secure method for identity verification, harnessing the power of blockchain's transparency and decentralization.

In regards to model selection for our speaker verification model, we spent a vast amount of resources looking at different models within the ASR and speaker verification realm. We found incompatibility with tensorflow as well as PyTorch models with the cartesi machine. We had limited model selections.

I-vectors are low-dimensional representations of variable-length speech segments, such as utterances or conversations. They capture the speaker-related information from the input speech signal and are used for speaker recognition tasks. The name "i-vector" comes from "identity vector" since they represent the identity or characteristics of the speaker.

Before extracting i-vectors, Kaldi typically processes the raw speech signal to extract various acoustic features, such as Mel-frequency cepstral coefficients (MFCCs), pitch features, and more. These features are used to represent the spectral content and other acoustic characteristics of the speech.

The first step in i-vector modeling involves training a Universal Background Model (UBM). The UBM is a Gaussian Mixture Model (GMM) that represents the distribution of all speakers in the training data. It provides a generic model of speaker-independent speech.

To capture the variability specific to each speaker, the i-vector model uses a subspace model. This subspace is represented by the Total Variability Matrix, also known as the Total Variability Matrix (T-Matrix). The T-Matrix captures the speaker-specific information beyond the UBM.

Given the UBM and T-Matrix, i-vectors are extracted from the adapted supervectors, which are the low-dimensional representations of the acoustic features. The i-vector extraction process projects these supervectors into the i-vector subspace, where they represent the speaker characteristics.

Once the i-vectors are obtained, they can be used for various speaker recognition tasks. In speaker verification, a similarity measure (e.g., cosine similarity) is used to compare two i-vectors and determine if they belong to the same speaker. In speaker diarization, i-vectors can be used to cluster segments of an audio recording by speaker identity.

One of the key advantages of using Cartesi for this process is the off-chain execution of the neural network model. Traditional blockchain environments struggle with the computational intensity of machine learning models, but Cartesi's off-chain computation capability allows for more complex and resource-intensive computations. This ensures efficient and timely verification of users' identities, enhancing the user experience and reducing verification times.

Furthermore, the Cartesi platform provides transparency and decentralization, which are crucial aspects of identity verification. All interactions and transactions related to the speaker verification process are recorded on the blockchain, ensuring a secure and tamper-proof audit trail. The decentralized nature of the blockchain also reduces the risk of central points of failure, making the system more resilient against potential attacks.

In conclusion, our on-chain speaker verification neural network model, deployed on the Cartesi platform, represents a pioneering approach to identity verification. By leveraging the unique characteristics of individuals' voices, we offer a robust and secure method for identity verification, harnessing the power of blockchain's transparency and decentralization. The potential applications of this technology are vast, promising improved security and trust in various sectors of the digital world. With continuous advancements in machine learning and blockchain technologies, our project holds the potential to revolutionize identity verification and pave the way for a more secure and seamless digital future.