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。

一种基于层次判别式 PLDA 的语音识别模型

A Discriminative Hierarchical PLDA-based Model for Spoken Language Recognition

Speaker embeddings (x-vectors) extracted from very short segments of speech
have recently been shown to give competitive performance in speaker
diarization. We generalize this recipe by extracting from each speech segment,
in parallel with the x-vector, also a diagonal precision matrix, thus providing
a path for the propagation of information about the quality of the speech
segment into a PLDA scoring backend. These precisions quantify the uncertainty
about what the values of the embeddings might have been if they had been
extracted from high quality speech segments. The proposed probabilistic
embeddings (x-vectors with precisions) are interfaced with the PLDA model by
treating the x-vectors as hidden variables and marginalizing them out. We apply
the proposed probabilistic embeddings as input to an agglomerative hierarchical
clustering (AHC) algorithm to do diarization in the DIHARD'19 evaluation set.
We compute the full PLDA likelihood 'by the book' for each clustering
hypothesis that is considered by AHC. We do joint discriminative training of
the PLDA parameters and of the probabilistic x-vector extractor. We demonstrate
accuracy gains relative to a baseline AHC algorithm, applied to traditional
xvectors (without uncertainty), and which uses averaging of binary
log-likelihood-ratios, rather than by-the-book scoring.

利用从短时间内的语音中提取的说话人 embedding（x-vectors）在说话人辨识任务中进行了竞争性表现，本文在该方案中提取了对角精度矩阵，用于衡量声音质量，并构建了概率说话人 embedding，将其作为输入应用于 AHC 算法用于辨识，接口与 PLDA 模型，实现对参数进行联合判别训练，与基础算法相比提高了精度。