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.
翻译:语音识别( SLR) 是指用于确定语言样本中语言的自动过程。 SLR 本身是一项重要的任务,例如,作为分析或分类大量多语种数据的工具。此外,它也是在工作流中选择下游应用程序的一个重要工具,例如选择适当的语音识别或机器翻译模式。 SLR 系统通常由两个阶段组成,一个是代表音频样本的嵌入过程,第二个是计算每种语言的最后分数。在这项工作中,我们将SLR 任务作为探测问题处理,将第二阶段作为概率性线性对立分析(PLDA)模式。我们表明,对PLDA参数的歧视性培训在通常的基因化培训方面有很大的收益。此外,我们提出一种新的等级化方法,即培训两种PLDA模型,一个是产生高度相关语言群集的分数,第二个是计算每个组的分数。最后语言检测分数是这两组分的组合,同时进行计算,第二个阶段间距分析,作为概率线性线性对调分析(PLDA) 模型显示一个不甚高等级的分数。我们所训练的高级测试的系统显示的是,一个高等级级级比重数据的收集。