Complexity and modeling power of insertion-deletion systems

SISTEMAS DE INSERCIÓN Y BORRADO: COMPLEJIDAD Y CAPACIDAD DE MODELADO El objetivo central de la tesis es el estudio de los sistemas de inserción y borrado y su capacidad computacional. Más concretamente, estudiamos algunos modelos de generación de lenguaje que usan operaciones de reescritura de dos cadenas. También consideramos una variante distribuida de los sistemas de inserción y borrado en el sentido de que las reglas se separan entre un número finito de nodos de un grafo. Estos sistemas se denominan sistemas controlados mediante grafo, y aparecen en muchas áreas de la Informática, jugando un papel muy importante en los lenguajes formales, la lingüística y la bio-informática. Estudiamos la decidibilidad/ universalidad de nuestros modelos mediante la variación de los parámetros de tamaño del vector. Concretamente, damos respuesta a la cuestión más importante concerniente a la expresividad de la capacidad computacional: si nuestro modelo es equivalente a una máquina de Turing o no. Abordamos sistemáticamente las cuestiones sobre los tamaños mínimos de los sistemas con y sin control de grafo.COMPLEXITY AND MODELING POWER OF INSERTION-DELETION SYSTEMS The central object of the thesis are insertion-deletion systems and their computational power. More specifically, we study language generating models that use two string rewriting operations: contextual insertion and contextual deletion, and their extensions. We also consider a distributed variant of insertion-deletion systems in the sense that rules are separated among a finite number of nodes of a graph. Such systems are refereed as graph-controlled systems. These systems appear in many areas of Computer Science and they play an important role in formal languages, linguistics, and bio-informatics. We vary the parameters of the vector of size of insertion-deletion systems and we study decidability/universality of obtained models. More precisely, we answer the most important questions regarding the expressiveness of the computational model: whether our model is Turing equivalent or not. We systematically approach the questions about the minimal sizes of the insertiondeletion systems with and without the graph-control

When Stars Control a Grammar's Work

Graph-controlled insertion-deletion (GCID) systems are regulated extensions of insertion-deletion systems. Such a system has several components and each component contains some insertion-deletion rules. The components are the vertices of a directed control graph. A rule is applied to a string in a component and the resultant string is moved to the target component specified in the rule. The language of the system is the set of all terminal strings collected in the final component. We impose the restriction in the structure of the underlying graph to be a star structure where there is a central, control component which acts like a master and transmits a string (after applying one of its rules) to one of the components specified in the (applied) rule. A component which receives the string can process the obtained string with any applicable rule available in it and sends back the resultant string only to the center component. With this restriction, we obtain computational completeness for some descriptional complexity measuresComment: In Proceedings AFL 2023, arXiv:2309.0112

Stabilising determinants in the transmission of phonotactic systems: Diachrony and acquisition of coda clusters in Dutch and Afrikaans

The phonotactic system of Afrikaans underwent multiple changes in its diachronic development. While some consonant clusters got lost, others still surface in contemporary Afrikaans. In this paper, we investigate to what extent articulatory difference between the segments of a cluster contribute to its successful transmission. We proceed in two steps. First, we analyse the respective effects of differences in manner of articulation, place of articulation and voicing on the age at which a cluster is acquired by analysing Dutch acquisition data. Second, we investigate the role that these articulatory differences play in the diachronic frequency development from Dutch to Afrikaans. We demonstrate that large differences in manner of articulation between segments contribute to a cluster’s success in acquisition and diachrony. In contrast, large differences in place of articulation have impeding effects, while voicing difference shows a more complicated behaviour.Keywords: Dutch/Afrikaans phonotactics, articulatory difference, first-language acquisition, diachronic chang

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

WAKE WORD DETECTION AND ITS APPLICATIONS

Always-on spoken language interfaces, e.g. personal digital assistants, rely on a wake word to start processing spoken input. Novel methods are proposed to train a wake word detection system from partially labeled training data, and to use it in on-line applications. In the system, the prerequisite of frame-level alignment is removed, permitting the use of un-transcribed training examples that are annotated only for the presence/absence of the wake word. Also, an FST-based decoder is presented to perform online detection. The suite of methods greatly improve the wake word detection performance across several datasets. A novel neural network for acoustic modeling in wake word detection is also investigated. Specifically, the performance of several variants of chunk-wise streaming Transformers tailored for wake word detection is explored, including looking-ahead to the next chunk, gradient stopping, different positional embedding methods and adding same-layer dependency between chunks. Experiments demonstrate that the proposed Transformer model outperforms the baseline convolutional network significantly with a comparable model size, while still maintaining linear complexity w.r.t. the input length. For the application of the detected wake word in ASR, the problem of improving speech recognition with the help of the detected wake word is investigated. Voice-controlled house-hold devices face the difficulty of performing speech recognition of device-directed speech in the presence of interfering background speech. Two end-to-end models are proposed to tackle this problem with information extracted from the anchored segment. The anchored segment refers to the wake word segment of the audio stream, which contains valuable speaker information that can be used to suppress interfering speech and background noise. A multi-task learning setup is also explored where the ideal mask, obtained from a data synthesis procedure, is used to guide the model training. In addition, a way to synthesize "noisy" speech from "clean" speech is also proposed to mitigate the mismatch between training and test data. The proposed methods show large word error reduction for Amazon Alexa live data with interfering background speech, without sacrificing the performance on clean speech