Integration of an RSA-2048-bit public key cryptography solution in the development of secure voice recognition processing applications

The authors initially employs the fast Fourier transform (FFT) approach to transforming voice inputs into digital signals before integrating a speech recognition solution (which includes two models: the hidden Markov model (HMM) and the artificial neural network (ANN)). To achieve standard-tone identification of voice signals and digitally store speech, the authors then incorporated a 2048-bit Rivest-Shamir-Adleman (RSA) encryption method to encrypt and decrypt digital speech. The authors’ building team constructed the program using a 256-bit advanced encryption standard - Galois counter mode (AES-GCM) encryption method to assure the application’s effectiveness. The authors successfully created a voice recognition application according to the HMM of ANN. The collected findings suggest that the authors’ secure speech recognition program (named soft voice - RSA) has improved in terms of safety, keeping speech material secret, and speed. It takes roughly 0.2 s to generate a 2048-bit RSA key pair that exceeds the National Institute of Standards and Technology (NIST) standard, 700-1070 ms to process speech, 1-4 ms to encrypt 2048-bit RSA, 6-8 ms to decrypt 2048-bit RSA

A novel hybrid method for vocal fold pathology diagnosis based on russian language

In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neighbours) and the different feature vectors (the initial and the optimized ones). Finally, a hybrid of the ensemble of decision tree and the genetic algorithm is proposed for vocal fold pathology diagnosis based on Russian Language. The experimental results show a better performance (the higher classification accuracy and the lower response time) of the proposed method in comparison with the others. While the usage of pure decision tree leads to the classification accuracy of 85.4% for vocal fold pathology diagnosis based on Russian language, the proposed method leads to the 8.5% improvement (the accuracy of 93.9%)

Herramienta de diagnóstico y evaluación para voces alaríngeas

Tras una laringectomía total, la calidad de la voz del paciente se degrada considerablemente, obteniendo como resultado lo que se denomina ¨voz alaríngea¨. Los pacientes de este tipo de cirugías necesitan asistir a un logopeda para la rehabilitación de la voz, aprendiendo y entrenando la forma de hablar de nuevo. En este proyecto se presenta una herramienta para la evaluación de voces alaríngeas. Para ello se realiza un análisis de la voz a estudiar evaluando ciertos parámetros característicos de este tipo de voces. En base a los parámetros a analizar, se ha diseñado la herramienta para posteriormente desarrollarla y finalmente comprobar su correcto funcionamiento con voces alaríngeas reales. La herramienta también representa los resultados para conseguir una visión rápida e intuitiva de las fortalezas y defectos de la voz analizada. Palabras clave: laringectomía total, rehabilitación de la voz, voz alaríngea, análisis de voz