Vowels and Prosody Contribution in Neural Network Based Voice Conversion Algorithm with Noisy Training Data

Abstract

Abstract-This research presents a neural network based voice conversion (VC) model. While it is a known fact that voiced sounds and prosody are the most important component of the voice conversion framework, what is not known is their objective contributions particularly in a noisy and uncontrolled environment. This model uses a 2-layer feedforward neural network to map the Linear prediction analysis coefficients of a source speaker to the acoustic vector space of the target speaker with a view to objectively determine the contributions of the voiced, unvoiced and supra-segmental components of sounds to the voice conversion model. Results showed that vowels "a", "i", "o" have the most significant contribution in the conversion success. The voiceless sounds were also found to be most affected by the noisy training data. An average noise level of 40 dB above the noise floor were found to degrade the voice conversion success by 55.14 percent relative to the voiced sounds. The result also shows that for cross-gender voice conversion, prosody conversion is more significant in scenarios where a female is the target speaker.

Performance Evaluation of LoRaWAN SX1276 Radio in Non-Line of Sight conditions

“…Efficient collision mitigation strategies, such as adaptive data rate algorithms and listen-before-talk protocols, are vital to ensure that LoRaWAN networks provide reliable, energy-efficient communication for a wide range of IoT applications. The advent of artificial intelligence like neural networks [15], [16] have been employed extensively in literature to advance LoRaWAN progress in mitigating collision-related challenges.…”