Spoken language recognition (SLR) refers to the automatic process used to
determine the language present in a speech sample. SLR is an important task in
its own right, for example, as a tool to analyze or categorize large amounts of
multi-lingual data. Further, it is also an essential tool for selecting
downstream applications in a work flow, for example, to chose appropriate
speech recognition or machine translation models. SLR systems are usually
composed of two stages, one where an embedding representing the audio sample is
extracted and a second one which computes the final scores for each language.
In this work, we approach the SLR task as a detection problem and implement the
second stage as a probabilistic linear discriminant analysis (PLDA) model. We
show that discriminative training of the PLDA parameters gives large gains with
respect to the usual generative training. Further, we propose a novel
hierarchical approach where two PLDA models are trained, one to generate scores
for clusters of highly-related languages and a second one to generate scores
conditional to each cluster. The final language detection scores are computed
as a combination of these two sets of scores. The complete model is trained
discriminatively to optimize a cross-entropy objective. We show that this
hierarchical approach consistently outperforms the non-hierarchical one for
detection of highly related languages, in many cases by large margins. We train
our systems on a collection of datasets including over 100 languages, and test
them both on matched and mismatched conditions, showing that the gains are
robust to condition mismatch.

本文介绍了一种基于概率线性判别分析模型的口语语言识别方法，该模型通过提取音频样本的嵌入向量得出语音信号的语言，并基于层次方法和最大熵准则进行训练，结果表明该识别方法可以用于高度相关的语言中并具有鲁棒性。