Summary of the 2015 NIST Language Recognition i-Vector Machine Learning Challenge

Abstract

In 2015 NIST coordinated the first language recognition evaluation (LRE) that used i-vectors as input, with the goals of attracting researchers outside of the speech processing community to tackle the language recognition problem, exploring new ideas in machine learning for use in language recognition, and improving recognition accuracy. The Language Recognition i-Vector Machine Learning Challenge, taking place over a period of four months, was well-received with 56 participants from 44 unique sites and over 3700 submissions, surpassing the participation levels of all previous traditional track LREs. The results of 46 of the 56 participants were better than the provided baseline system, with the best system achieving approximately 55% relative improvement over the baseline.

Stacked Long-Term TDNN for Spoken Language Recognition

“…In 2015 NIST coordinated the first language recognition evaluation that distributed i-vectors to participants [12]. The speech data used to produce the i-vectors was collected over the telephone channel (telephone conversations and narrowband broadcasts) and comprised 65 languages.…”