Biblio

Following review depicts a unique speech recognition technique, based on planned analysis and utilization of Neural Network and Google API using speech’s characteristics. Multifactor security system pioneered for the authentication of vocal modalities and identification. Undergone project drives completely unique strategy of independent convolution layers structure and involvement of totally unique convolutions includes spectrum and Mel-frequency cepstral coefficient. This review takes in the statistical analysis of sound using scaled up and scaled down spectrograms, conjointly by exploitation the Google Speech-to-text API turns speech to pass code, it will be cross-verified for extended security purpose. Our study reveals that the incorporated methodology and the result provided elucidate the inclination of research in this area and encouraged us to advance in this field.

Security often requires that the data must be kept safe from unauthorized access. And the best line of speech communication is security. However, most computers are interconnected with each other openly, thereby exposing them and the communication channels that person uses. Speech cryptography secures information by protecting its confidentiality. It can also be used to protect information about the integrity and authenticity of data. Stronger cryptographic techniques are needed to ensure the integrity of data stored on a machine that may be infected or under attack. So far speech cryptography is used in many forms but using it with Audio file is another stronger technique. The process of cryptography happens with audio file for transferring more secure sensitive data. The audio file is encrypted and decrypted by using Lorenz 3D mapping and then 3D mapping function is converted into 2D mapping function by using euler's numerical resolution and strong algorithm provided by using henon mapping and then decrypted by using reverse of encryption. By implementing this, the resultant audio file will be in secured form.