Advances on the Transcription of Historical Manuscripts based on Multimodality, Interactivity and Crowdsourcing

Natural Language Processing (NLP) is an interdisciplinary research field of Computer Science, Linguistics, and Pattern Recognition that studies, among others, the use of human natural languages in Human-Computer Interaction (HCI). Most of NLP research tasks can be applied for solving real-world problems. This is the case of natural language recognition and natural language translation, that can be used for building automatic systems for document transcription and document translation. Regarding digitalised handwritten text documents, transcription is used to obtain an easy digital access to the contents, since simple image digitalisation only provides, in most cases, search by image and not by linguistic contents (keywords, expressions, syntactic or semantic categories). Transcription is even more important in historical manuscripts, since most of these documents are unique and the preservation of their contents is crucial for cultural and historical reasons. The transcription of historical manuscripts is usually done by paleographers, who are experts on ancient script and vocabulary. Recently, Handwritten Text Recognition (HTR) has become a common tool for assisting paleographers in their task, by providing a draft transcription that they may amend with more or less sophisticated methods. This draft transcription is useful when it presents an error rate low enough to make the amending process more comfortable than a complete transcription from scratch. Thus, obtaining a draft transcription with an acceptable low error rate is crucial to have this NLP technology incorporated into the transcription process. The work described in this thesis is focused on the improvement of the draft transcription offered by an HTR system, with the aim of reducing the effort made by paleographers for obtaining the actual transcription on digitalised historical manuscripts. This problem is faced from three different, but complementary, scenarios: · Multimodality: The use of HTR systems allow paleographers to speed up the manual transcription process, since they are able to correct on a draft transcription. Another alternative is to obtain the draft transcription by dictating the contents to an Automatic Speech Recognition (ASR) system. When both sources (image and speech) are available, a multimodal combination is possible and an iterative process can be used in order to refine the final hypothesis. · Interactivity: The use of assistive technologies in the transcription process allows one to reduce the time and human effort required for obtaining the actual transcription, given that the assistive system and the palaeographer cooperate to generate a perfect transcription. Multimodal feedback can be used to provide the assistive system with additional sources of information by using signals that represent the whole same sequence of words to transcribe (e.g. a text image, and the speech of the dictation of the contents of this text image), or that represent just a word or character to correct (e.g. an on-line handwritten word). · Crowdsourcing: Open distributed collaboration emerges as a powerful tool for massive transcription at a relatively low cost, since the paleographer supervision effort may be dramatically reduced. Multimodal combination allows one to use the speech dictation of handwritten text lines in a multimodal crowdsourcing platform, where collaborators may provide their speech by using their own mobile device instead of using desktop or laptop computers, which makes it possible to recruit more collaborators.El Procesamiento del Lenguaje Natural (PLN) es un campo de investigación interdisciplinar de las Ciencias de la Computación, Lingüística y Reconocimiento de Patrones que estudia, entre otros, el uso del lenguaje natural humano en la interacción Hombre-Máquina. La mayoría de las tareas de investigación del PLN se pueden aplicar para resolver problemas del mundo real. Este es el caso del reconocimiento y la traducción del lenguaje natural, que se pueden utilizar para construir sistemas automáticos para la transcripción y traducción de documentos. En cuanto a los documentos manuscritos digitalizados, la transcripción se utiliza para facilitar el acceso digital a los contenidos, ya que la simple digitalización de imágenes sólo proporciona, en la mayoría de los casos, la búsqueda por imagen y no por contenidos lingüísticos. La transcripción es aún más importante en el caso de los manuscritos históricos, ya que la mayoría de estos documentos son únicos y la preservación de su contenido es crucial por razones culturales e históricas. La transcripción de manuscritos históricos suele ser realizada por paleógrafos, que son personas expertas en escritura y vocabulario antiguos. Recientemente, los sistemas de Reconocimiento de Escritura (RES) se han convertido en una herramienta común para ayudar a los paleógrafos en su tarea, la cual proporciona un borrador de la transcripción que los paleógrafos pueden corregir con métodos más o menos sofisticados. Este borrador de transcripción es útil cuando presenta una tasa de error suficientemente reducida para que el proceso de corrección sea más cómodo que una completa transcripción desde cero. Por lo tanto, la obtención de un borrador de transcripción con una baja tasa de error es crucial para que esta tecnología de PLN sea incorporada en el proceso de transcripción. El trabajo descrito en esta tesis se centra en la mejora del borrador de transcripción ofrecido por un sistema RES, con el objetivo de reducir el esfuerzo realizado por los paleógrafos para obtener la transcripción de manuscritos históricos digitalizados. Este problema se enfrenta a partir de tres escenarios diferentes, pero complementarios: · Multimodalidad: El uso de sistemas RES permite a los paleógrafos acelerar el proceso de transcripción manual, ya que son capaces de corregir en un borrador de la transcripción. Otra alternativa es obtener el borrador de la transcripción dictando el contenido a un sistema de Reconocimiento Automático de Habla. Cuando ambas fuentes están disponibles, una combinación multimodal de las mismas es posible y se puede realizar un proceso iterativo para refinar la hipótesis final. · Interactividad: El uso de tecnologías asistenciales en el proceso de transcripción permite reducir el tiempo y el esfuerzo humano requeridos para obtener la transcripción correcta, gracias a la cooperación entre el sistema asistencial y el paleógrafo para obtener la transcripción perfecta. La realimentación multimodal se puede utilizar en el sistema asistencial para proporcionar otras fuentes de información adicionales con señales que representen la misma secuencia de palabras a transcribir (por ejemplo, una imagen de texto, o la señal de habla del dictado del contenido de dicha imagen de texto), o señales que representen sólo una palabra o carácter a corregir (por ejemplo, una palabra manuscrita mediante una pantalla táctil). · Crowdsourcing: La colaboración distribuida y abierta surge como una poderosa herramienta para la transcripción masiva a un costo relativamente bajo, ya que el esfuerzo de supervisión de los paleógrafos puede ser drásticamente reducido. La combinación multimodal permite utilizar el dictado del contenido de líneas de texto manuscrito en una plataforma de crowdsourcing multimodal, donde los colaboradores pueden proporcionar las muestras de habla utilizando su propio dispositivo móvil en lugar de usar ordenadores,El Processament del Llenguatge Natural (PLN) és un camp de recerca interdisciplinar de les Ciències de la Computació, la Lingüística i el Reconeixement de Patrons que estudia, entre d'altres, l'ús del llenguatge natural humà en la interacció Home-Màquina. La majoria de les tasques de recerca del PLN es poden aplicar per resoldre problemes del món real. Aquest és el cas del reconeixement i la traducció del llenguatge natural, que es poden utilitzar per construir sistemes automàtics per a la transcripció i traducció de documents. Quant als documents manuscrits digitalitzats, la transcripció s'utilitza per facilitar l'accés digital als continguts, ja que la simple digitalització d'imatges només proporciona, en la majoria dels casos, la cerca per imatge i no per continguts lingüístics (paraules clau, expressions, categories sintàctiques o semàntiques). La transcripció és encara més important en el cas dels manuscrits històrics, ja que la majoria d'aquests documents són únics i la preservació del seu contingut és crucial per raons culturals i històriques. La transcripció de manuscrits històrics sol ser realitzada per paleògrafs, els quals són persones expertes en escriptura i vocabulari antics. Recentment, els sistemes de Reconeixement d'Escriptura (RES) s'han convertit en una eina comuna per ajudar els paleògrafs en la seua tasca, la qual proporciona un esborrany de la transcripció que els paleògrafs poden esmenar amb mètodes més o menys sofisticats. Aquest esborrany de transcripció és útil quan presenta una taxa d'error prou reduïda perquè el procés de correcció siga més còmode que una completa transcripció des de zero. Per tant, l'obtenció d'un esborrany de transcripció amb un baixa taxa d'error és crucial perquè aquesta tecnologia del PLN siga incorporada en el procés de transcripció. El treball descrit en aquesta tesi se centra en la millora de l'esborrany de la transcripció ofert per un sistema RES, amb l'objectiu de reduir l'esforç realitzat pels paleògrafs per obtenir la transcripció de manuscrits històrics digitalitzats. Aquest problema s'enfronta a partir de tres escenaris diferents, però complementaris: · Multimodalitat: L'ús de sistemes RES permet als paleògrafs accelerar el procés de transcripció manual, ja que són capaços de corregir un esborrany de la transcripció. Una altra alternativa és obtenir l'esborrany de la transcripció dictant el contingut a un sistema de Reconeixement Automàtic de la Parla. Quan les dues fonts (imatge i parla) estan disponibles, una combinació multimodal és possible i es pot realitzar un procés iteratiu per refinar la hipòtesi final. · Interactivitat: L'ús de tecnologies assistencials en el procés de transcripció permet reduir el temps i l'esforç humà requerits per obtenir la transcripció real, gràcies a la cooperació entre el sistema assistencial i el paleògraf per obtenir la transcripció perfecta. La realimentació multimodal es pot utilitzar en el sistema assistencial per proporcionar fonts d'informació addicionals amb senyals que representen la mateixa seqüencia de paraules a transcriure (per exemple, una imatge de text, o el senyal de parla del dictat del contingut d'aquesta imatge de text), o senyals que representen només una paraula o caràcter a corregir (per exemple, una paraula manuscrita mitjançant una pantalla tàctil). · Crowdsourcing: La col·laboració distribuïda i oberta sorgeix com una poderosa eina per a la transcripció massiva a un cost relativament baix, ja que l'esforç de supervisió dels paleògrafs pot ser reduït dràsticament. La combinació multimodal permet utilitzar el dictat del contingut de línies de text manuscrit en una plataforma de crowdsourcing multimodal, on els col·laboradors poden proporcionar les mostres de parla utilitzant el seu propi dispositiu mòbil en lloc d'utilitzar ordinadors d'escriptori o portàtils, la qual cosa permet ampliar el nombrGranell Romero, E. (2017). Advances on the Transcription of Historical Manuscripts based on Multimodality, Interactivity and Crowdsourcing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86137TESI

Handwriting versus keyboarding in first grade: Which modality best supports written composition performance and learning?

PhD thesis in Reading researchAn important background for the present thesis is the increasing digitalisation in school, and more specifically, the Norwegian first-grade reality, where a growing number of schools provide students with personal digital devices to be used in initial writing instruction. The research that compares effects of handwriting and keyboarding on children’s early writing is, however, scarce, findings are inconsistent, and many of the studies suffer from methodological problems, for example, inadequate control of children’s prewriting experience (Wollscheid et al., 2016). The aim of the present thesis was therefore to investigate whether modality – handwriting on paper or keyboarding on digital tablet with text-to-speech functionality – affects first grader’s written composition performance and written composition learning, and whether these effects depend on children’s literacy skills (grapheme-phoneme mapping, first sound segmentation, blending, word reading, spelling and vocabulary) measured at school start. This was examined in a sample of Norwegian first graders from 18 schools, where five schools taught children to write by hand, five schools taught children to write by digital tablet postponing handwriting, and eight schools taught children to write both by hand and using a digital tablet. Children’s compositions were analysed for length and quality by formally assessing a set of text features related to both transcription (spacing, spelling and punctuation) and narrative sophistication (vocabulary, syntax and narrative structures). The text quality measures were specifically developed for assessing narratives by beginning writers which typically are short and simple. The statistical modelling was done using Bayesian methods, which allow for demonstrating evidence in both the presence and absence of effects. This thesis includes four articles. Article 1 is a philosophical discussion of how texts by beginning writers can be analysed from a quantitative viewpoint. The three remaining articles contribute to the thesis by empirically investigating the effects of modality on first graders’ written composition performance and written composition learning. Article 2 shows that first graders who are taught writing in both modalities from the start of school are likely to produce compositions of similar length and quality in both modalities. This article also shows that the lack of a modality effect on written composition performance does not depend on children’s literacy skills. For example, students with weaker literacy skills did not produce stories of higher quality in one or another modality. Article 3 demonstrates that first-grade students receiving instruction based on handwriting or digital tablets with otherwise minimal change to instruction, overall learn to compose text at the same rate throughout the first year of formal writing instruction. The students showed similar development in text length, syntactic complexity and accuracy, and narrative structures, regardless of learning to write by hand or with a digital tablet. Students writing with a digital tablet showed better performance in transcription accuracy (spelling, spacing and terminator accuracy), but showed little or no development of these text features through the first grade. Students writing by hand started at a lower performance level for transcription accuracy but showed improvement throughout the year. This difference in performance can probably be attributed to the text-to-speech functionality offered by the digital tablets. Article 4 shows that there were no interaction effects between modality and students’ literacy skills on learning to compose text. This means that there were, for example, no advantages related to learning to compose text with a digital tablet, or by hand, for students with weaker literacy skills. The conclusion of the thesis is that, in a context similar to the one studied here, modality does not substantially affect first-grade students’ written composition performance or written composition learning. Thus, it seems that instruction based on handwriting and instruction based on digital tablets can provide children with similar opportunities to develop their written composition skills in their first year of school. Before clear recommendations about the choice of modality for initial writing instruction can be made, future research should investigate the potential transition effects of going from learning to write in one modality to the other.En viktig bakgrunn for denne avhandlingen er den økende digitaliseringen i skolen, og mer spesifikt den norske første-klasse-virkeligheten, der et økende antall skoler utstyrer elevene med personlige digitale enheter til bruk i skriveopplæringen. Forskningen som sammenligner effektene av håndskrift og tastaturskriving på barns tidlige skriving er imidlertid knapp, funn er inkonsistente og mange av studiene lider av metodologiske svakheter, for eksempel utilstrekkelig kontroll av deltakernes tidligere skriveerfaring (Wollscheid et al., 2016). Målet med denne avhandlingen var derfor å undersøke om modalitet – håndskrift på papir eller tastaturskriving på nettbrett med tekst-til-tale funksjonalitet – påvirker førsteklassingers prestasjon i og læring av tekstkomposisjon, og om disse modalitetseffektene avhenger av barnas literacyferdigheter (grafem-fonem-kunnskap, framlydsanalyse, fonologisk syntese, ordlesing, staving og vokabular) målt ved skolestart. Dette ble undersøkt i et utvalg av norske førsteklassinger fra 18 skoler, hvorav fem skoler lærte barna å skrive for hånd, fem skoler utsatte håndskriftsopplæringen og lærte elevene å skrive på digitalt nettbrett, og åtte skoler lærte barna å skrive både for hånd og på digitalt nettbrett. Elevenes tekster ble analysert for lengde og kvalitet gjennom formell vurdering av et sett av teksttrekk knyttet både til transkripsjon (staving, mellomromsbruk og tegnsetting) og narrativ kompleksitet (vokabular, syntaks og narrative strukturer). Tekstkvalitetsmålene ble utviklet spesielt for å vurdere begynnerskriveres fortellinger, som typisk er korte og enkle. Den statistiske analysen ble gjort gjennom Bayesianske metoder, som kan bevise både tilstedeværelse og fravær av effekter. Avhandlingen inkluderer fire artikler. Artikkel 1 er en vitenskapsteoretisk diskusjon av hvordan tekster av begynnerskrivere kan analyseres fra et kvantitativt perspektiv. De tre resterende artiklene bidrar til avhandlingen gjennom å empirisk undersøke modalitetseffekter på førsteklassingers prestasjon i og læring av tekstkomposisjon. Artikkel 2 gir evidens for at førsteklassinger, som fra starten av første klasse lærer å skrive i begge modaliteter, etter all sannsynlighet produserer fortellinger av lik lengde og kvalitet i begge modaliteter. Denne artikkelen viser også at mangelen på en modalitetseffekt på prestasjon i tekstkomposisjon ikke avhenger av elevenes literacyferdigheter. For eksempel skrev ikke elever med svakere literacyferdigheter fortellinger av høyere kvalitet i en av modalitetene. Artikkel 3 viser at førsteklasseelever som får undervisning basert på enten håndskrift eller digitalt nettbrett, med ellers minimal forandring i undervisningen, i hovedsak lærer å komponere tekster i samme takt gjennom det første året med skriveopplæring. Elevene viste lik utvikling av tekstlengde, syntaktisk kompleksitet og nøyaktighet og narrative strukturer, uavhengig av om de lærte å skrive for hånd eller på digitalt nettbrett. Elever som skrev på nettbrett, presterte bedre på transkripsjonsnøyaktighet (stave-, mellomroms- og tegnsettingsnøyaktighet), men viste liten eller ingen utvikling av disse teksttrekkene gjennom førsteklasse. Elever som skrev for hånd, startet på et lavere nivå i transkripsjonsnøyaktighet, men viste utvikling gjennom året. Denne forskjellen i prestasjon kan sannsynligvis tilskrives tekst-til-tale funksjonaliteten på de digitale nettbrettene. Artikkel 4 viser at det ikke var noen interaksjonseffekter mellom modalitet og elevenes literacyferdigheter på læring av tekstkomposisjon. Det vil si at det var, for eksempel, ingen fordeler knyttet til å lære å komponere tekst på digitalt nettbrett, eller for hånd, for elever med svakere literacyferdigheter. Konklusjonen i avhandlingen er at, i en kontekst lik den som er studert her, påvirker ikke modalitet førsteklassingers prestasjon i tekstkomposisjon eller læring av tekstkomposisjon i vesentlig grad. Det ser altså ut som at skriveopplæring basert på håndskrift og skriveopplæring basert på nettbrett kan gi elever like muligheter for å utvikle ferdigheter i tekstkomposisjon det første året på skolen. Før klare anbefalinger om bruk av modalitet i begynneropplæring kan gis, bør framtidig forskning undersøke mulige overgangseffekter i å gå fra å lære å skrive i en modalitet til den andre modaliteten

Multimodal interaction with mobile devices : fusing a broad spectrum of modality combinations

This dissertation presents a multimodal architecture for use in mobile scenarios such as shopping and navigation. It also analyses a wide range of feasible modality input combinations for these contexts. For this purpose, two interlinked demonstrators were designed for stand-alone use on mobile devices. Of particular importance was the design and implementation of a modality fusion module capable of combining input from a range of communication modes like speech, handwriting, and gesture. The implementation is able to account for confidence value biases arising within and between modalities and also provides a method for resolving semantically overlapped input. Tangible interaction with real-world objects and symmetric multimodality are two further themes addressed in this work. The work concludes with the results from two usability field studies that provide insight on user preference and modality intuition for different modality combinations, as well as user acceptance for anthropomorphized objects.Diese Dissertation präsentiert eine multimodale Architektur zum Gebrauch in mobilen Umständen wie z. B. Einkaufen und Navigation. Außerdem wird ein großes Gebiet von möglichen modalen Eingabekombinationen zu diesen Umständen analysiert. Um das in praktischer Weise zu demonstrieren, wurden zwei teilweise gekoppelte Vorführungsprogramme zum \u27stand-alone\u27; Gebrauch auf mobilen Geräten entworfen. Von spezieller Wichtigkeit war der Entwurf und die Ausführung eines Modalitäts-fusion Modul, das die Kombination einer Reihe von Kommunikationsarten wie Sprache, Handschrift und Gesten ermöglicht. Die Ausführung erlaubt die Veränderung von Zuverlässigkeitswerten innerhalb einzelner Modalitäten und außerdem ermöglicht eine Methode um die semantisch überlappten Eingaben auszuwerten. Wirklichkeitsnaher Dialog mit aktuellen Objekten und symmetrische Multimodalität sind zwei weitere Themen die in dieser Arbeit behandelt werden. Die Arbeit schließt mit Resultaten von zwei Feldstudien, die weitere Einsicht erlauben über die bevorzugte Art verschiedener Modalitätskombinationen, sowie auch über die Akzeptanz von anthropomorphisierten Objekten

Multimodal interaction with mobile devices : fusing a broad spectrum of modality combinations

This dissertation presents a multimodal architecture for use in mobile scenarios such as shopping and navigation. It also analyses a wide range of feasible modality input combinations for these contexts. For this purpose, two interlinked demonstrators were designed for stand-alone use on mobile devices. Of particular importance was the design and implementation of a modality fusion module capable of combining input from a range of communication modes like speech, handwriting, and gesture. The implementation is able to account for confidence value biases arising within and between modalities and also provides a method for resolving semantically overlapped input. Tangible interaction with real-world objects and symmetric multimodality are two further themes addressed in this work. The work concludes with the results from two usability field studies that provide insight on user preference and modality intuition for different modality combinations, as well as user acceptance for anthropomorphized objects.Diese Dissertation präsentiert eine multimodale Architektur zum Gebrauch in mobilen Umständen wie z. B. Einkaufen und Navigation. Außerdem wird ein großes Gebiet von möglichen modalen Eingabekombinationen zu diesen Umständen analysiert. Um das in praktischer Weise zu demonstrieren, wurden zwei teilweise gekoppelte Vorführungsprogramme zum 'stand-alone'; Gebrauch auf mobilen Geräten entworfen. Von spezieller Wichtigkeit war der Entwurf und die Ausführung eines Modalitäts-fusion Modul, das die Kombination einer Reihe von Kommunikationsarten wie Sprache, Handschrift und Gesten ermöglicht. Die Ausführung erlaubt die Veränderung von Zuverlässigkeitswerten innerhalb einzelner Modalitäten und außerdem ermöglicht eine Methode um die semantisch überlappten Eingaben auszuwerten. Wirklichkeitsnaher Dialog mit aktuellen Objekten und symmetrische Multimodalität sind zwei weitere Themen die in dieser Arbeit behandelt werden. Die Arbeit schließt mit Resultaten von zwei Feldstudien, die weitere Einsicht erlauben über die bevorzugte Art verschiedener Modalitätskombinationen, sowie auch über die Akzeptanz von anthropomorphisierten Objekten

Multi-modal post-editing of machine translation

As MT quality continues to improve, more and more translators switch from traditional translation from scratch to PE of MT output, which has been shown to save time and reduce errors. Instead of mainly generating text, translators are now asked to correct errors within otherwise helpful translation proposals, where repetitive MT errors make the process tiresome, while hard-to-spot errors make PE a cognitively demanding activity. Our contribution is three-fold: first, we explore whether interaction modalities other than mouse and keyboard could well support PE by creating and testing the MMPE translation environment. MMPE allows translators to cross out or hand-write text, drag and drop words for reordering, use spoken commands or hand gestures to manipulate text, or to combine any of these input modalities. Second, our interviews revealed that translators see value in automatically receiving additional translation support when a high CL is detected during PE. We therefore developed a sensor framework using a wide range of physiological and behavioral data to estimate perceived CL and tested it in three studies, showing that multi-modal, eye, heart, and skin measures can be used to make translation environments cognition-aware. Third, we present two multi-encoder Transformer architectures for APE and discuss how these can adapt MT output to a domain and thereby avoid correcting repetitive MT errors.Angesichts der stetig steigenden Qualität maschineller Übersetzungssysteme (MÜ) post-editieren (PE) immer mehr Übersetzer die MÜ-Ausgabe, was im Vergleich zur herkömmlichen Übersetzung Zeit spart und Fehler reduziert. Anstatt primär Text zu generieren, müssen Übersetzer nun Fehler in ansonsten hilfreichen Übersetzungsvorschlägen korrigieren. Dennoch bleibt die Arbeit durch wiederkehrende MÜ-Fehler mühsam und schwer zu erkennende Fehler fordern die Übersetzer kognitiv. Wir tragen auf drei Ebenen zur Verbesserung des PE bei: Erstens untersuchen wir, ob andere Interaktionsmodalitäten als Maus und Tastatur das PE unterstützen können, indem wir die Übersetzungsumgebung MMPE entwickeln und testen. MMPE ermöglicht es, Text handschriftlich, per Sprache oder über Handgesten zu verändern, Wörter per Drag & Drop neu anzuordnen oder all diese Eingabemodalitäten zu kombinieren. Zweitens stellen wir ein Sensor-Framework vor, das eine Vielzahl physiologischer und verhaltensbezogener Messwerte verwendet, um die kognitive Last (KL) abzuschätzen. In drei Studien konnten wir zeigen, dass multimodale Messung von Augen-, Herz- und Hautmerkmalen verwendet werden kann, um Übersetzungsumgebungen an die KL der Übersetzer anzupassen. Drittens stellen wir zwei Multi-Encoder-Transformer-Architekturen für das automatische Post-Editieren (APE) vor und erörtern, wie diese die MÜ-Ausgabe an eine Domäne anpassen und dadurch die Korrektur von sich wiederholenden MÜ-Fehlern vermeiden können.Deutsche Forschungsgemeinschaft (DFG), Projekt MMP

Application of Machine Learning within Visual Content Production

We are living in an era where digital content is being produced at a dazzling pace. The heterogeneity of contents and contexts is so varied that a numerous amount of applications have been created to respond to people and market demands. The visual content production pipeline is the generalisation of the process that allows a content editor to create and evaluate their product, such as a video, an image, a 3D model, etc. Such data is then displayed on one or more devices such as TVs, PC monitors, virtual reality head-mounted displays, tablets, mobiles, or even smartwatches. Content creation can be simple as clicking a button to film a video and then share it into a social network, or complex as managing a dense user interface full of parameters by using keyboard and mouse to generate a realistic 3D model for a VR game. In this second example, such sophistication results in a steep learning curve for beginner-level users. In contrast, expert users regularly need to refine their skills via expensive lessons, time-consuming tutorials, or experience. Thus, user interaction plays an essential role in the diffusion of content creation software, primarily when it is targeted to untrained people. In particular, with the fast spread of virtual reality devices into the consumer market, new opportunities for designing reliable and intuitive interfaces have been created. Such new interactions need to take a step beyond the point and click interaction typical of the 2D desktop environment. The interactions need to be smart, intuitive and reliable, to interpret 3D gestures and therefore, more accurate algorithms are needed to recognise patterns. In recent years, machine learning and in particular deep learning have achieved outstanding results in many branches of computer science, such as computer graphics and human-computer interface, outperforming algorithms that were considered state of the art, however, there are only fleeting efforts to translate this into virtual reality. In this thesis, we seek to apply and take advantage of deep learning models to two different content production pipeline areas embracing the following subjects of interest: advanced methods for user interaction and visual quality assessment. First, we focus on 3D sketching to retrieve models from an extensive database of complex geometries and textures, while the user is immersed in a virtual environment. We explore both 2D and 3D strokes as tools for model retrieval in VR. Therefore, we implement a novel system for improving accuracy in searching for a 3D model. We contribute an efficient method to describe models through 3D sketch via an iterative descriptor generation, focusing both on accuracy and user experience. To evaluate it, we design a user study to compare different interactions for sketch generation. Second, we explore the combination of sketch input and vocal description to correct and fine-tune the search for 3D models in a database containing fine-grained variation. We analyse sketch and speech queries, identifying a way to incorporate both of them into our system's interaction loop. Third, in the context of the visual content production pipeline, we present a detailed study of visual metrics. We propose a novel method for detecting rendering-based artefacts in images. It exploits analogous deep learning algorithms used when extracting features from sketches

The Challenge of Spoken Language Systems: Research Directions for the Nineties

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area