Speech translation (ST) is the task of directly translating acoustic speech signals in a source language into text in a foreign language. ST task has been addressed, for a long time, using a pipeline approach with two modules : first an Automatic Speech Recognition (ASR) in the source language followed by a text-to-text Machine translation (MT). In the past few years, we have seen a paradigm shift towards the end-to-end approaches using sequence-to-sequence deep neural network models. This paper presents our efforts towards the development of the first Broadcast News end-to-end Arabic to English speech translation system. Starting from independent ASR and MT LDC releases, we were able to identify about 92 hours of Arabic audio recordings for which the manual transcription was also translated into English at the segment level. These data was used to train and compare pipeline and end-to-end speech translation systems under multiple scenarios including transfer learning and data augmentation techniques.

In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. These two datasets were made available for the IWSLT 2022 low-resource speech translation track, and they consist of collections of radio recordings from the Studio Kalangou (Niger) and Studio Tamani (Mali) daily broadcast news. We share (i) a massive amount of unlabeled audio data (671 hours) in five languages: French from Niger, Fulfulde, Hausa, Tamasheq and Zarma, and (ii) a smaller parallel corpus of audio recordings (17 hours) in Tamasheq, with utterance-level translations in the French language. All this data is shared under the Creative Commons BY-NC-ND 3.0 license. We hope these resources will inspire the speech community to develop and benchmark models using the Tamasheq language.

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.

本文介绍了一个测试台，以研究表现出群体智能的无人机（UAV）的分布式传感问题。几种智能城市应用程序，例如运输和灾难响应，需要通过一群智能和合作的无人机有效地收集传感器数据。事实证明，对于系统和严格研究而没有损害规模，现实主义和外部有效性，这通常被证明太复杂且昂贵。借助拟议的测试床，本文设置了一个垫脚石，以在小实验室空间内模仿，源自经验数据和仿真模型的大型感应区域。在此感应地图上，一群低成本的无人机可以飞行，从而可以研究各种问题，例如能源消耗，充电控制，导航和避免碰撞。分散的多代理集体学习算法（EPO）适用于无人机群智能以及对功耗测量的评估提供了概念验证，并验证了拟议的测试台的准确性。