We investigate what linguistic factors affect the performance of Automatic
Speech Recognition (ASR) models. We hypothesize that orthographic and
phonological complexities both degrade accuracy. To examine this, we fine-tune
the multilingual self-supervised pretrained model Wav2Vec2-XLSR-53 on 25
languages with 15 writing systems, and we compare their ASR accuracy, number of
graphemes, unigram grapheme entropy, logographicity (how much
word/morpheme-level information is encoded in the writing system), and number
of phonemes. The results demonstrate that orthographic complexities
significantly correlate with low ASR accuracy, while phonological complexity
shows no significant correlation.

在这篇研究中，我们调查了哪些语言因素会影响自动语音识别（ASR）模型的性能。我们假设正字法复杂性和音素复杂性都会降低准确性。为了验证这一假设，我们在 25 种语言的基础上，使用 15 种书写体系对多语言自我监督预训练模型 Wav2Vec2-XLSR-53 进行微调，并比较它们的 ASR 准确性、字素数量、一元字素熵、表意性（书写系统中编码了多少词 / 语素级信息）以及音素数量。研究结果表明，正字法复杂性与低 ASR 准确性显著相关，而音素复杂性则没有显著相关性。

语言复杂性与语音识别准确性：字母复杂性有害，音韵复杂性无害

Language Complexity and Speech Recognition Accuracy: Orthographic  Complexity Hurts, Phonological Complexity Doesn't

Automatic Speech Recognition (ASR) systems exhibit the best performance on
speech that is similar to that on which it was trained. As such,
underrepresented varieties including regional dialects, minority-speakers, and
low-resource languages, see much higher word error rates (WERs) than those
varieties seen as 'prestigious', 'mainstream', or 'standard'. This can act as a
barrier to incorporating ASR technology into the annotation process for
large-scale linguistic research since the manual correction of the erroneous
automated transcripts can be just as time and resource consuming as manual
transcriptions. A deeper understanding of the behaviour of an ASR system is
thus beneficial from a speech technology standpoint, in terms of improving ASR
accuracy, and from an annotation standpoint, where knowing the likely errors
made by an ASR system can aid in this manual correction. This work demonstrates
a method of probing an ASR system to discover how it handles phonetic variation
across a number of L2 Englishes. Specifically, how particular phonetic
realisations which were rare or absent in the system's training data can lead
to phoneme level misrecognitions and contribute to higher WERs. It is
demonstrated that the behaviour of the ASR is systematic and consistent across
speakers with similar spoken varieties (in this case the same L1) and phoneme
substitution errors are typically in agreement with human annotators. By
identifying problematic productions specific weaknesses can be addressed by
sourcing such realisations for training and fine-tuning thus making the system
more robust to pronunciation variation.

使用一种探测方法，发现了 ASR 系统处理在 L2 英语方言中的发音差异时，出现了声素级别的错误并产生了更高的 WER。这项工作系统地说明了 ASR 的行为，通过确定特定问题的物质来源来提高 ASR 的准确性。

探究自动语音识别系统对 L2 英语语音变异的敏感性

Investigating the Sensitivity of Automatic Speech Recognition Systems to  Phonetic Variation in L2 Englishes

Public sources like parliament meeting recordings and transcripts provide
ever-growing material for the training and evaluation of automatic speech
recognition (ASR) systems. In this paper, we publish and analyse the Finnish
parliament ASR corpus, the largest publicly available collection of manually
transcribed speech data for Finnish with over 3000 hours of speech and 449
speakers for which it provides rich demographic metadata. This corpus builds on
earlier initial work, and as a result the corpus has a natural split into two
training subsets from two periods of time. Similarly, there are two official,
corrected test sets covering different times, setting an ASR task with
longitudinal distribution-shift characteristics. An official development set is
also provided. We develop a complete Kaldi-based data preparation pipeline, and
hidden Markov model (HMM), hybrid deep neural network (HMM-DNN) and
attention-based encoder-decoder (AED) ASR recipes. We set benchmarks on the
official test sets, as well as multiple other recently used test sets. Both
temporal corpus subsets are already large, and we observe that beyond their
scale, ASR performance on the official test sets plateaus, whereas other
domains benefit from added data. The HMM-DNN and AED approaches are compared in
a carefully matched equal data setting, with the HMM-DNN system consistently
performing better. Finally, the variation of the ASR accuracy is compared
between the speaker categories available in the parliament metadata to detect
potential biases based on factors such as gender, age, and education.

本文介绍了芬兰议会 ASR 语料库及相关的 Kaldi-based 数据准备管道，HMM，HMM-DNN 和 AED ASR 配方，以及通过测试集和其他数据集在 ASR 上设置了基准，最后比较了不同议会元数据的 ASR 准确性变化。