一种基于判别式分层PLDA的口语识别模型
A Discriminative Hierarchical PLDA-based Model for Spoken Language Recognition
摘要：
口语识别（SLR）是指用于确定语音样本中存在的语言的自动过程。SLR本身就是一项重要的任务，例如，作为分析或分类大量多语言数据的工具。此外，它也是在工作流中选择下游应用的必要工具，例如，选择适当的语音识别或机器翻译模型。SLR系统通常由两个阶段组成，一个是提取代表音频样本的嵌入，第二个是计算每种语言的最终分数。在这项工作中，我们接近SLR任务作为一个检测问题，并实现第二阶段作为一个概率线性判别分析（PLDA）模型。我们发现，PLDA参数的判别式训练相对于通常的生成训练提供了很大的增益。此外，我们提出了一种新的分层方法，其中训练两个PLDA模型，一个用于生成高度相关语言的聚类的分数，第二个用于生成每个聚类的分数条件。最终的语言检测分数被计算为这两组分数的组合。完整的模型是有区别地训练的，以优化交叉熵目标。我们表明，这种层次化的方法始终优于非层次化的高度相关的语言检测，在许多情况下，大幅度。我们训练我们的系统上收集的数据集，包括超过100种语言，并测试他们的匹配和不匹配的条件下，显示的收益是强大的条件不匹配。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
