Post-ASR LLM based Speaker Tagging Leaderboard

What is Post-ASR LLM based Speaker Tagging Leaderboard ?

The Post-ASR LLM based Speaker Tagging Leaderboard is a tool designed to evaluate and compare the performance of Large Language Models (LLMs) on speaker tagging tasks. It focuses on processing outputs from Automatic Speech Recognition (ASR) systems to identify and tag speakers in audio data. This leaderboard provides a platform to benchmark different LLMs and their ability to accurately assign speaker tags in transcribed audio content.

Features

• Submission Interface: Allows users to submit their model evaluations for speaker tagging tasks.
• Leaderboard Visualization: Displays the performance of various LLMs in a clear and comparative format.
• Performance Metrics: Provides detailed metrics such as accuracy, precision, recall, and F1-score for speaker tagging.
• Model Comparison: Enables users to compare the performance of different LLMs on the same dataset.
• Customizable Filtering: Users can filter results based on specific models, datasets, or evaluation criteria.
• Detailed Analytics: Offers in-depth analysis of errors and success cases for model improvement.

How to use Post-ASR LLM based Speaker Tagging Leaderboard ?

1. Prepare Your Model: Train and fine-tune your LLM for speaker tagging tasks using your preferred dataset.
2. Run Speaker Tagging: Process your ASR output through your LLM to generate speaker tags for the transcribed audio.
3. Submit Results: Upload your model's speaker tagging results to the leaderboard through the provided interface.
4. View Performance: Check your model's ranking and performance metrics on the leaderboard.
5. Analyze Results: Use the provided analytics to identify strengths, weaknesses, and areas for improvement.

Frequently Asked Questions

What is speaker tagging?
Speaker tagging is the process of identifying and assigning labels to different speakers in an audio transcription, allowing for the differentiation of dialogue between multiple participants.

How do I submit my model's results to the leaderboard?
Submit your model's speaker tagging results through the leaderboard's submission interface, ensuring your data is formatted according to the specified requirements.

What metrics are used to evaluate performance on the leaderboard?
The leaderboard uses standard metrics such as accuracy, precision, recall, and F1-score to evaluate speaker tagging performance. These metrics provide a comprehensive view of your model's effectiveness.