Tuplemax Loss for Language Identification

ABSTRACT In many scenarios of a language identification task, the user will specify a small set of languages which he/she can speak instead of a large set of all possible languages. We want to model such prior knowledge into the way we train our neural networks, by replacing the commonly used softmax loss function with a novel loss function named tuplemax loss. As a matter of fact, a typical language identification system launched in North America has about 95% users who could speak no more than two languages. Using the tuplemax loss, our system achieved a 2 . Index Terms-- Language identification, tuplemax loss, LSTM 1. INTRODUCTION Large vocabulary continuous speech recognition (L VCSR) systems are becoming increasingly relevant for industry, tracking the technological trend toward increased human interaction using voice-operated devices [1].