Nuclear Confrontations

The effect of nuclear weapons has long been debated. Some argue that these weapons have a stabilizing effect on already volatile regions and rivals, while others fear that it will only further escalate tensions. This undergraduate thesis studies how relations between countries have changed once a country has attained nuclear weapons. Specifically, whether Militarized Interstate Disputes (MIDs) have increased or decreased before and after the acquisition of nuclear weapons. In order to do so, I look at the severity and occurrence of MIDs to see trends in changes of state attitudes because of nuclear weapons, through the lenses of the three most popular nuclear schools of thought: deterrence, stability/instability paradox and irrelevance.Ope

South Asia: After the Bomb

The effect of nuclear weapons has long been debated. Some argue that these weapons have a stabilizing effect on already volatile regions and rivals, while others fear that it will only further escalate tensions. In their book debating a nuclear world, Waltz and Sagan take opposing positions on the effect that the atom bomb had on the territorial issues India has with its neighbors Pakistan and China. The case of South Asia and the bomb is unique in the sense that while they are all ancient civilizations, their current regimes are all the same age. We are thus offered a cocktail of ancient civilizations, young regimes, territorial conflict, and history’s most lethal weapon. This paper seeks to discover if the presence of nuclear bombs has impacted the ability to resolve territorial disputes between these nations. After looking at the foreign policy of each country and the history of their development of nuclear weapons, I find that, yes, possessing nuclear weapons has delayed the resolution of this territorial issue.Ope

On the Existence of Elementwise Invariant Vectors in Representations of Symmetric Groups

We determine when a permutation with cycle type $\mu$ admits a non-zero invariant vector in the irreducible representation $V_\lambda$ of the symmetric group. We find that a majority of pairs $(\lambda,\mu)$ have this property, with only a few simple exceptions.Comment: 16 pages, 11 figure

Automatic Speech Analysis Framework for ATC Communication in HAAWAII

Over the past years, several SESAR funded exploratory projects focused on bringing speech and language technologies to the Air Traffic Management (ATM) domain and demonstrating their added value through successful applications. Recently ended HAAWAII project developed a generic architecture and framework, which was validated through several tasks such as callsign highlighting, pre-filling radar labels, and readback error detection. The primary goal was to support pilot and air traffic controller communication by deploying Automatic Speech Recognition (ASR) engines. Contextual information (if available) extracted from surveillance data, flight plan data, or previous communication can be exploited via entity boosting to further improve the recognition performance. HAAWAII proposed various design attributes to integrate the ASR engine into the ATM framework, often depending on concrete technical specifics of target air navigation service providers (ANSPs). This paper gives a brief overview and provides an objective assessment of speech processing components developed and integrated into the HAAWAII framework. Specifically, the following tasks are evaluated w.r.t. application domain: (i) speech activity detection, (ii) speaker segmentation and speaker role classification, as well as (iii) ASR. To our best knowledge, HAAWAII framework offers the best performing speech technologies for ATM, reaching high recognition accuracy (i.e., error-correction done by exploiting additional contextual data), robustness (i.e., models developed using large training corpora) and support for rapid domain transfer (i.e., to new ATM sector with minimum investment). Two scenarios provided by ANSPs were used for testing, achieving callsign detection accuracy of about 96% and 95% for NATS and ISAVIA, respectively

Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding

Voice communication between air traffic controllers (ATCos) and pilots is critical for ensuring safe and efficient air traffic control (ATC). This task requires high levels of awareness from ATCos and can be tedious and error-prone. Recent attempts have been made to integrate artificial intelligence (AI) into ATC in order to reduce the workload of ATCos. However, the development of data-driven AI systems for ATC demands large-scale annotated datasets, which are currently lacking in the field. This paper explores the lessons learned from the ATCO2 project, a project that aimed to develop a unique platform to collect and preprocess large amounts of ATC data from airspace in real time. Audio and surveillance data were collected from publicly accessible radio frequency channels with VHF receivers owned by a community of volunteers and later uploaded to Opensky Network servers, which can be considered an "unlimited source" of data. In addition, this paper reviews previous work from ATCO2 partners, including (i) robust automatic speech recognition, (ii) natural language processing, (iii) English language identification of ATC communications, and (iv) the integration of surveillance data such as ADS-B. We believe that the pipeline developed during the ATCO2 project, along with the open-sourcing of its data, will encourage research in the ATC field. A sample of the ATCO2 corpus is available on the following website: https://www.atco2.org/data, while the full corpus can be purchased through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. We demonstrated that ATCO2 is an appropriate dataset to develop ASR engines when little or near to no ATC in-domain data is available. For instance, with the CNN-TDNNf kaldi model, we reached the performance of as low as 17.9% and 24.9% WER on public ATC datasets which is 6.6/7.6% better than "out-of-domain" but supervised CNN-TDNNf model.Comment: Manuscript under revie

Grammar Based Speaker Role Identification for Air Traffic Control Speech Recognition

Automatic Speech Recognition (ASR) for air traffic control is generally trained by pooling Air Traffic Controller (ATCO) and pilot data. In practice, this is motivated by the proportion of annotated data from pilots being less than ATCO’s. However, due to the data imbalance of ATCO and pilot and their varying acoustic conditions, the ASR performance is usually significantly better for ATCOs speech than pilots. Obtaining the speaker roles requires manual effort when the voice recordings are collected using Very High Frequency (VHF) receivers and the data is noisy and in a single channel without the push-totalk (PTT) signal. In this paper, we propose to (1) split the ATCO and pilot data using an intuitive approach exploiting ASR transcripts and (2) consider ATCO and pilot ASR as two separate tasks for Acoustic Model (AM) training. The paper focuses on applying this approach to noisy data collected using VHF receivers, as this data is helpful for training despite its noisy nature. We also developed a simple yet efficient knowledgebased system for speaker role classification based on grammar defined by the International Civil Aviation Organization (ICAO). Our system accepts as input text, thus, either gold annotations or transcripts generated by an ABSR system. This approach provides an average accuracy in speaker role identification of 83%. Finally, we show that training AMs separately for each task, or using a multitask approach, is well suited for the noisy data compared to the traditional ASR system, where all data is pooled together for AM training

How to Measure Speech Recognition Performance in the Air Traffic Control Domain? The Word Error Rate is only half of the truth

Applying Automatic Speech Recognition (ASR) in the domain of analogue voice communication between air traffic controllers (ATCo) and pilots has more end user requirements than just transforming spoken words into text. It is useless, when word recognition is perfect, as long as the semantic interpretation is wrong. For an ATCo it is of no importance if the words of greeting are correctly recognized. A wrong recognition of a greeting should, however, not disturb the correct recognition of e.g. a “descend” command. Recently, 14 European partners from Air Traffic Management (ATM) domain have agreed on a common set of rules, i.e., an ontology on how to annotate the speech utterance of an ATCo. This paper first extends the ontology to pilot utterances and then compares different ASR implementations on semantic level by introducing command recognition, command recognition error, and command rejection rates. The implementation used in this paper achieves a command recognition rate better than 94% for Prague Approach, even when WER is above 2.5

ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications

Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dialogues are carried between an air traffic controller (ATCO) and pilots via very-high frequency radio channels. In order to incorporate these novel technologies into ATC (low-resource domain), large-scale annotated datasets are required to develop the data-driven AI systems. Two examples are automatic speech recognition (ASR) and natural language understanding (NLU). In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data. The ATCO2 corpus covers 1) data collection and pre-processing, 2) pseudo-annotations of speech data, and 3) extraction of ATC-related named entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set corpus contains 4 hours of ATC speech with manual transcripts and a subset with gold annotations for named-entity recognition (callsign, command, value). 2) The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched with automatic transcripts from an in-domain speech recognizer, contextual information, speaker turn information, signal-to-noise ratio estimate and English language detection score per sample. Both available for purchase through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. 3) The ATCO2-test-set-1h corpus is a one-hour subset from the original test set corpus, that we are offering for free at https://www.atco2.org/data. We expect the ATCO2 corpus will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community.Comment: Manuscript under review; The code will be available at https://github.com/idiap/atco2-corpu

How Does Pre-trained Wav2Vec2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications

Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few works investigated the impact on performance when the data substantially differs between the pre-training and downstream fine-tuning phases (i.e., domain shift). We target this scenario by analyzing the robustness of Wav2Vec2.0 and XLS-R models on downstream ASR for a completely unseen domain, i.e., air traffic control (ATC) communications. We benchmark the proposed models on four challenging ATC test sets (signal-to-noise ratio varies between 5 to 20 dB). Relative word error rate (WER) reduction between 20% to 40% are obtained in comparison to hybrid-based state-of-the-art ASR baselines by fine-tuning E2E acoustic models with a small fraction of labeled data. We also study the impact of fine-tuning data size on WERs, going from 5 minutes (few-shot) to 15 hours.Comment: This paper has been submitted to Interspeech 202