In 2022, the U.S. National Institute of Standards and Technology (NIST)
conducted the latest Language Recognition Evaluation (LRE) in an ongoing series
administered by NIST since 1996 to foster research in language recognition and
to measure state-of-the-art technology. Similar to previous LREs, LRE22 focused
on conversational telephone speech (CTS) and broadcast narrowband speech (BNBS)
data. LRE22 also introduced new evaluation features, such as an emphasis on
African languages, including low resource languages, and a test set consisting
of segments containing between 3s and 35s of speech randomly sampled and
extracted from longer recordings. A total of 21 research organizations, forming
16 teams, participated in this 3-month long evaluation and made a total of 65
valid system submissions to be evaluated. This paper presents an overview of
LRE22 and an analysis of system performance over different evaluation
conditions. The evaluation results suggest that Oromo and Tigrinya are easier
to detect while Xhosa and Zulu are more challenging. A greater confusability is
seen for some language pairs. When speech duration increased, system
performance significantly increased up to a certain duration, and then a
diminishing return on system performance is observed afterward.

本文概述与分析了 2022 年美国国家标准与技术研究所进行的最新语言识别评估 (LRE22) 的系统表现，发现使用不同评估条件下 Oromo 和 Tigrinya 较容易识别，而 Xhosa 和 Zulu 则较为困难。此外，当语音时长增加时，系统性能显著提高，但随着时间的增加，性能提高的速度逐渐变慢。

2022 年 NIST 语言识别评估

The 2022 NIST Language Recognition Evaluation

Automatic dubbing (AD) is the task of translating the original speech in a
video into target language speech. The new target language speech should
satisfy isochrony; that is, the new speech should be time aligned with the
original video, including mouth movements, pauses, hand gestures, etc. In this
paper, we propose training a model that directly optimizes both the translation
as well as the speech duration of the generated translations. We show that this
system generates speech that better matches the timing of the original speech,
compared to prior work, while simplifying the system architecture.

本研究提出了一种自动配音的模型，通过同时进行翻译和语音持续时间的优化，实现生成的配音更好地匹配原始视频的时间轴，同时简化了整个系统的结构。

联合优化翻译和语音时序以改善自动配音中的等时间性

Jointly Optimizing Translations and Speech Timing to Improve Isochrony in Automatic Dubbing

Video dubbing aims to translate the original speech in a film or television
program into the speech in a target language, which can be achieved with a
cascaded system consisting of speech recognition, machine translation and
speech synthesis. To ensure the translated speech to be well aligned with the
corresponding video, the length/duration of the translated speech should be as
close as possible to that of the original speech, which requires strict length
control. Previous works usually control the number of words or characters
generated by the machine translation model to be similar to the source
sentence, without considering the isochronicity of speech as the speech
duration of words/characters in different languages varies. In this paper, we
propose a machine translation system tailored for the task of video dubbing,
which directly considers the speech duration of each token in translation, to
match the length of source and target speech. Specifically, we control the
speech length of generated sentence by guiding the prediction of each word with
the duration information, including the speech duration of itself as well as
how much duration is left for the remaining words. We design experiments on
four language directions (German -> English, Spanish -> English, Chinese <->
English), and the results show that the proposed method achieves better length
control ability on the generated speech than baseline methods. To make up the
lack of real-world datasets, we also construct a real-world test set collected
from films to provide comprehensive evaluations on the video dubbing task.

该研究针对影视配音中原始语音和目标语音的对齐问题，提出一种机器翻译系统，通过考虑语音时长控制翻译长度，构建了真实世界情景下的测试集，实现了对自然长度的更好控制。