Real-time Voice Adaptation with Abstract Normalization and Sound-indexed Based Search

This paper proposes a two-step system to conduct real-time voice adaptation in the field of speech processing. The first step includes recording and pre-processing to form a voice profile. Secondly is real-time input of the voice and adapting the input into a target voice. Concerning the fact that individual voices’ structure are habitually varying, this paper suggests a method for converting them into a comparable format. The new method is called abstract normalization which cuts the voice data into smaller sounds. From the sounds are generated an abstracted, simplified version of the data using a level of abstraction along with parameter fitting. The normalized data is used to generate a sound-index which consists of a sequence hash that represents the current object in a simpler fashion. The indices are used to compare different sounds/voices for adaptation. This effectively transforms the speech-related challenges into a search problem rather than a biometric one. To assess the approach, voice profile data are compared against each other as a method to verify the sound-index. Lastly a real-time voice input using alternating levels of abstraction is run against a voice profile created with Norwegian words. The degree of adaptation success is measured in percentage, and experimental results show that while accuracy is not yet excellent, the concept was validated