Tech Stack: C#, Python

Expert’s cases:

1. R&D in Speech Processing using Deep Learning Methods: TTS, voice conversion, speaker recognition
   Key achievements:

   • Developed and trained a bimodal (speech and face) verification MVP solution using PyTorch, reducing the Equal Error Rate (EER) from 1.01% (speech only) to 0.34% (speech+face)

   • Collected and preprocessed large datasets using SQL, PySpark, and clustering methods for testing and fine-tuning the release model.

   • Leveraged expertise in PyTorch, computer vision, knowledge from paper overviews, and experiments with different training methods/architectures to develop an alternative speaker recognition solution, achieving a significant (1.1--2x) improvement in False Rejection Rate (FRR) compared to the baseline and competitors' solutions

   • Created a zero-shot speech synthesis solution using PyTorch, HuBERT model, and Variational Autoencoder-based Text-to-Speech (TTS) model. Achieved a remarkable 70% relative improvement in speaker similarity and significantly improved robustness to noise in the reference speech

   • Developed a noisy-robust zero-shot speech synthesis method by modifying the VITS architecture. Introduced a self-supervised DINO loss for joint training of a speaker verification model and a unit-based VITS, resulting in substantial enhancements in robustness and speaker similarity. Currently under review at ICASSP 2024

   
   

2. Development of a predictive system based on ML, statistical, and optimization methods
   Technologies: C#, Python
   Key Achievements:
   - Implemented features for associated petroleum gas production forecasting using C#, DBSCAN, linear regression, and derivative-free optimization methods
   - Developed a feature for estimating required volumes of water injection into wells using C#, linear regression, and numerical methods for solving differential equations
   - Conducted code refactoring and test coverage, resulting in a significant reduction in the number of bugs in the production solution
   
   The developed methods have successfully contributed to forming and improving the accuracy of forecasts for geologists