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。