Speech-to-text (S2T) summarization is a time-saving technique for filtering
and keeping up with the broadcast news uploaded online on a daily basis. The
rise of large language models from deep learning with impressive text
generation capabilities has placed the research focus on summarization systems
that produce paraphrased compact versions of the document content, also known
as abstractive summaries. End-to-end (E2E) modelling of S2T abstractive
summarization is a promising approach that offers the possibility of generating
rich latent representations that leverage non-verbal and acoustic information,
as opposed to the use of only linguistic information from automatically
generated transcripts in cascade systems. However, the few literature on E2E
modelling of this task fails on exploring different domains, namely broadcast
news, which is challenging domain where large and diversified volumes of data
are presented to the user every day. We model S2T summarization both with a
cascade and an E2E system for a corpus of broadcast news in French. Our novel
E2E model leverages external data by resorting to transfer learning from a
pre-trained T2T summarizer. Experiments show that both our cascade and E2E
abstractive summarizers are stronger than an extractive baseline. However, the
performance of the E2E model still lies behind the cascade one, which is object
of an extensive analysis that includes future directions to close that gap.

将语音文字（S2T）摘要分为级联和端到端两种模型，并使用预训练的 T2T 摘要提取器进行迁移学习，进而将其应用于在线上传的广播新闻中。结果表明，端到端模型要略逊于级联模型，但都优于提取式基线模型。

走向端到端的语音转文本摘要

Towards End-to-end Speech-to-text Summarization

Several high-resource Text to Speech (TTS) systems currently produce natural,
well-established human-like speech. In contrast, low-resource languages,
including Arabic, have very limited TTS systems due to the lack of resources.
We propose a fully unsupervised method for building TTS, including automatic
data selection and pre-training/fine-tuning strategies for TTS training, using
broadcast news as a case study. We show how careful selection of data, yet
smaller amounts, can improve the efficiency of TTS system in generating more
natural speech than a system trained on a bigger dataset. We adopt to propose
different approaches for the: 1) data: we applied automatic annotations using
DNSMOS, automatic vowelization, and automatic speech recognition (ASR) for
fixing transcriptions' errors; 2) model: we used transfer learning from
high-resource language in TTS model and fine-tuned it with one hour broadcast
recording then we used this model to guide a FastSpeech2-based Conformer model
for duration. Our objective evaluation shows 3.9% character error rate (CER),
while the groundtruth has 1.3% CER. As for the subjective evaluation, where 1
is bad and 5 is excellent, our FastSpeech2-based Conformer model achieved a
mean opinion score (MOS) of 4.4 for intelligibility and 4.2 for naturalness,
where many annotators recognized the voice of the broadcaster, which proves the
effectiveness of our proposed unsupervised method.

本文探讨了一种针对低资源语言的完全无监督的 TTS 建模方法，旨在提高语音生成的效率并让其更接近自然人类语音。在案例研究中，该方法成功地使用了广播新闻作为语料集，通过自动数据选择和预训练 / 微调策略，提高了模型的性能。

以阿拉伯广播新闻为案例研究的 TTS 无监督数据选择

Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study

This paper describes an enhanced automatic keyphrase extraction method
applied to Broadcast News. The keyphrase extraction process is used to create a
concept level for each news. On top of words resulting from a speech
recognition system output and news indexation and it contributes to the
generation of a tag/keyphrase cloud of the top news included in a Multimedia
Monitoring Solution system for TV and Radio news/programs, running daily, and
monitoring 12 TV channels and 4 Radios.

本文介绍了一种加强的自动关键词提取方法，应用于广播新闻。该方法利用语音识别和新闻索引等方式，为每条新闻创建一个概念层，同时生成一个包括电视和广播新闻节目中的关键词云。