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2023 INTERSPEECH INTERSPEECH 2023

Description and analysis of the KPT system for NIST Language Recognition Evaluation 2022

Abstract

This paper presents an analysis of the KPT system for the 2022 NIST Language Recognition Evaluation. The KPT submission focuses on the fixed training condition where only specific speech data can be used to develop all the modules and auxiliary systems used to build the language recognizer. Our solution consists of several sub-systems based on different neural network front-ends and a common back-end for classification and fusion. The goal of each front-end is to extract language-related embeddings. Gaussian linear models are used to classify the embeddings of each front-end, followed by multi-class logistic regression to calibrate and fuse the different sub-systems. Experimental results from the NIST LRE 2022 evaluation task show that our approach achieves competitive performance.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
🧭 Keyword Pioneer — neural network frontend

Authors

Salvatore Sarni , Sandro Cumani , Sabato Marco Siniscalchi , Andrea Bottino

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Classification Speech & Audio > Recognition > Speech Recognition Machine Learning > Core Methods > Ensemble Methods

Keywords

logistic regression embedding learning model fusion language recognition gaussian linear model neural network system fusion neural network frontend
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