A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges
  in Russian Speech Generative Models
Authors: name: Kirill Borodin, name: Nikita Vasiliev, name: Vasiliy Kudryavtsev, name: Maxim Maslov, name: Mikhail Gorodnichev, name: Oleg Rogov, name: Grach Mkrtchian

Russian speech synthesis presents distinctive challenges, including vowel
reduction, consonant devoicing, variable stress patterns, homograph ambiguity,
and unnatural intonation. This paper introduces Balalaika, a novel dataset
comprising more than 2,000 hours of studio-quality Russian speech with
comprehensive textual annotations, including punctuation and stress markings.
Experimental results show that models trained on Balalaika significantly
outperform those trained on existing datasets in both speech synthesis and
enhancement tasks. We detail the dataset construction pipeline, annotation
methodology, and results of comparative evaluations.