This paper presents a novel algorithm for building an automatic speech
recognition (ASR) model with imperfect training data. Imperfectly transcribed
speech is a prevalent issue in human-annotated speech corpora, which degrades
the performance of ASR models. To address this problem, we propose Bypass
Temporal Classification (BTC) as an expansion of the Connectionist Temporal
Classification (CTC) criterion. BTC explicitly encodes the uncertainties
associated with transcripts during training. This is accomplished by enhancing
the flexibility of the training graph, which is implemented as a weighted
finite-state transducer (WFST) composition. The proposed algorithm improves the
robustness and accuracy of ASR systems, particularly when working with
imprecisely transcribed speech corpora. Our implementation will be
open-sourced.

提出了一种利用 Bypass Temporal Classification (BTC) 方法来扩大微调自动语音识别模型 (CNN) 准确性的算法，并且该算法构建了一个基于加权有限状态转换器 (WFST) 的可变性训练图来明确地编码训练期间的不确定性，从而改善了处理精度不高的语音资源库时 ASR 系统的健壮性和准确性。

弱监督不完美转录下的自动语音识别：绕过时间分类

Bypass Temporal Classification: Weakly Supervised Automatic Speech  Recognition with Imperfect Transcripts

Sample re-weighting strategies provide a promising mechanism to deal with
imperfect training data in machine learning, such as noisily labeled or
class-imbalanced data. One such strategy involves formulating a bi-level
optimization problem called the meta re-weighting problem, whose goal is to
optimize performance on a small set of perfect pivotal samples, called meta
samples. Many approaches have been proposed to efficiently solve this problem.
However, all of them assume that a perfect meta sample set is already provided
while we observe that the selections of meta sample set is performance
critical. In this paper, we study how to learn to identify such a meta sample
set from a large, imperfect training set, that is subsequently cleaned and used
to optimize performance in the meta re-weighting setting. We propose a learning
framework which reduces the meta samples selection problem to a weighted
K-means clustering problem through rigorously theoretical analysis. We propose
two clustering methods within our learning framework, Representation-based
clustering method (RBC) and Gradient-based clustering method (GBC), for
balancing performance and computational efficiency. Empirical studies
demonstrate the performance advantage of our methods over various baseline
methods.

本研究提出了一种学习框架，将元样本选择问题通过严格的理论分析降低到加权 K 均值聚类问题中，提出了两种聚类方法 RBC 和 GBC 以解决 “样本权重不均匀” 问题，并在实际应用中得到了验证。

元重置中的关键样本选择学习

Learning to Select Pivotal Samples for Meta Re-weighting

We study the effect of imperfect training data labels on the performance of
classification methods. In a general setting, where the probability that an
observation in the training dataset is mislabelled may depend on both the
feature vector and the true label, we bound the excess risk of an arbitrary
classifier trained with imperfect labels in terms of its excess risk for
predicting a noisy label. This reveals conditions under which a classifier
trained with imperfect labels remains consistent for classifying uncorrupted
test data points. Furthermore, under stronger conditions, we derive detailed
asymptotic properties for the popular $k$-nearest neighbour ($k$nn), support
vector machine (SVM) and linear discriminant analysis (LDA) classifiers. One
consequence of these results is that the knn and SVM classifiers are robust to
imperfect training labels, in the sense that the rate of convergence of the
excess risks of these classifiers remains unchanged; in fact, our theoretical
and empirical results even show that in some cases, imperfect labels may
improve the performance of these methods. On the other hand, the LDA classifier
is shown to be typically inconsistent in the presence of label noise unless the
prior probabilities of each class are equal. Our theoretical results are
supported by a simulation study.

研究了不完美的训练数据标签对分类方法性能的影响，确立了一些条件，使得 knn 和 SVM 分类器对不完美标签有着鲁棒性并在某些情况下表现更佳，而 LDA 分类器因标签噪声而不一致，理论结果得到了模拟研究的支持。