Parliamentary and legislative debate transcripts provide an exciting insight
into elected politicians' opinions, positions, and policy preferences. They are
interesting for political and social sciences as well as linguistics and
natural language processing (NLP). Exiting research covers discussions within
individual parliaments. In contrast, we apply advanced NLP methods to a joint
and comparative analysis of six national parliaments (Bulgarian, Czech, French,
Slovene, Spanish, and United Kingdom) between 2017 and 2020, whose transcripts
are a part of the ParlaMint dataset collection. Using a uniform methodology, we
analyze topics discussed, emotions, and sentiment. We assess if the age,
gender, and political orientation of speakers can be detected from speeches.
The results show some commonalities and many surprising differences among the
analyzed countries.

本文通过先进的 NLP 方法，联合比较分析了保加利亚、捷克、法国、斯洛文尼亚、西班牙和英国等 6 个国家在 2017-2020 年间的议会和立法辩论记录，探讨并评估不同国家议员的观点、情感以及性别、政治取向的识别，发现分析对象之间存在许多差异和共性。

多方面、多语言和跨语言议会演讲分析

Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysis

The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with
recordings of meetings from Stortinget, the Norwegian parliament. It is the
first, publicly available dataset containing unscripted, Norwegian speech
designed for training of automatic speech recognition (ASR) systems. The
recordings are manually transcribed and annotated with language codes and
speakers, and there are detailed metadata about the speakers. The
transcriptions exist in both normalized and non-normalized form, and
non-standardized words are explicitly marked and annotated with standardized
equivalents. To test the usefulness of this dataset, we have compared an ASR
system trained on the NPSC with a baseline system trained on only
manuscript-read speech. These systems were tested on an independent dataset
containing spontaneous, dialectal speech. The NPSC-trained system performed
significantly better, with a 22.9% relative improvement in word error rate
(WER). Moreover, training on the NPSC is shown to have a "democratizing" effect
in terms of dialects, as improvements are generally larger for dialects with
higher WER from the baseline system.

使用挪威议会语音语料库进行自动语音识别训练较手稿朗读语音训练效果更好，尤其对方言的识别表现更出色。