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

The goal of this work is to train effective representations for keyword
spotting via metric learning. Most existing works address keyword spotting as a
closed-set classification problem, where both target and non-target keywords
are predefined. Therefore, prevailing classifier-based keyword spotting systems
perform poorly on non-target sounds which are unseen during the training stage,
causing high false alarm rates in real-world scenarios. In reality, keyword
spotting is a detection problem where predefined target keywords are detected
from a variety of unknown sounds. This shares many similarities to metric
learning problems in that the unseen and unknown non-target sounds must be
clearly differentiated from the target keywords. However, a key difference is
that the target keywords are known and predefined. To this end, we propose a
new method based on metric learning that maximises the distance between target
and non-target keywords, but also learns per-class weights for target keywords
\`a la classification objectives. Experiments on the Google Speech Commands
dataset show that our method significantly reduces false alarms to unseen
non-target keywords, while maintaining the overall classification accuracy.

通过度量学习训练关键词识别的有效表示，提出一种新的基于度量学习的方法，可以对目标关键词和非目标关键词进行准确的识别，从而显著降低未知非目标关键词的误报率，并保持整体分类准确性。

关键词检测的度量学习

Metric Learning for Keyword Spotting

Determining dense semantic correspondences across objects and scenes is a
difficult problem that underpins many higher-level computer vision algorithms.
Unlike canonical dense correspondence problems which consider images that are
spatially or temporally adjacent, semantic correspondence is characterized by
images that share similar high-level structures whose exact appearance and
geometry may differ.
Motivated by object recognition literature and recent work on rapidly
estimating linear classifiers, we treat semantic correspondence as a
constrained detection problem, where an exemplar LDA classifier is learned for
each pixel. LDA classifiers have two distinct benefits: (i) they exhibit higher
average precision than similarity metrics typically used in correspondence
problems, and (ii) unlike exemplar SVM, can output globally interpretable
posterior probabilities without calibration, whilst also being significantly
faster to train.
We pose the correspondence problem as a graphical model, where the unary
potentials are computed via convolution with the set of exemplar classifiers,
and the joint potentials enforce smoothly varying correspondence assignment.

本文探讨了将语义对应视为约束检测问题的可能性，介绍了使用 LDA 分类器学习每个像素点的方法，并将该问题表述为图模型。