How to improve TTS systems for emotional expressivity

Several experiments have been carried out that revealed weaknesses of the current Text-To-Speech (TTS) systems in their emotional expressivity. Although some TTS systems allow XML-based representations of prosodic and/or phonetic variables, few publications considered, as a pre-processing stage, the use of intelligent text processing to detect affective information that can be used to tailor the parameters needed for emotional expressivity. This paper describes a technique for an automatic prosodic parameterization based on affective clues. This technique recognizes the affective information conveyed in a text and, accordingly to its emotional connotation, assigns appropriate pitch accents and other prosodic parameters by XML-tagging. This pre-processing assists the TTS system to generate synthesized speech that contains emotional clues. The experimental results are encouraging and suggest the possibility of suitable emotional expressivity in speech synthesis

Expressivity in TTS from Semantics and Pragmatics

In this paper we present ongoing work to produce an expressive TTS reader that can be used both in text and dialogue applications. The system called SPARSAR has been used to read (English) poetry so far but it can now be applied to any text. The text is fully analyzed both at phonetic and phonological level, and at syntactic and semantic level. In addition, the system has access to a restricted list of typical pragmatically marked phrases and expressions that are used to convey specific discourse function and speech acts and need specialized intonational contours. The text is transformed into a poem-like structures, where each line corresponds to a Breath Group, semantically and syntactically consistent. Stanzas correspond to paragraph boundaries. Analogical parameters are related to ToBI theoretical in- dices but their number is doubled. In this paper, we concentrate on short stories and fables

Investigating and extending the methods in automated opinion analysis through improvements in phrase based analysis

Opinion analysis is an area of research which deals with the computational treatment of opinion statement and subjectivity in textual data. Opinion analysis has emerged over the past couple of decades as an active area of research, as it provides solutions to the issues raised by information overload. The problem of information overload has emerged with the advancements in communication technologies which gave rise to an exponential growth in user generated subjective data available online. Opinion analysis has a rich set of applications which are used to enable opportunities for organisations such as tracking user opinions about products, social issues in communities through to engagement in political participation etc.The opinion analysis area shows hyperactivity in recent years and research at different levels of granularity has, and is being undertaken. However it is observed that there are limitations in the state-of-the-art, especially as dealing with the level of granularities on their own does not solve current research issues. Therefore a novel sentence level opinion analysis approach utilising clause and phrase level analysis is proposed. This approach uses linguistic and syntactic analysis of sentences to understand the interdependence of words within sentences, and further uses rule based analysis for phrase level analysis to calculate the opinion at each hierarchical structure of a sentence. The proposed opinion analysis approach requires lexical and contextual resources for implementation. In the context of this Thesis the approach is further presented as part of an extended unifying framework for opinion analysis resulting in the design and construction of a novel corpus. The above contributions to the field (approach, framework and corpus) are evaluated within the Thesis and are found to make improvements on existing limitations in the field, particularly with regards to opinion analysis automation. Further work is required in integrating a mechanism for greater word sense disambiguation and in lexical resource development

A conceptual framework for an affective tutoring system using unobtrusive affect sensing for enhanced tutoring outcomes

PhD ThesisAffect plays a pivotal role in influencing the student’s motivation and learning achievements. The ability of expert human tutors to achieve enhanced learning outcomes is widely attributed to their ability to sense the affect of their tutees and to continually adapt their tutoring strategies in response to the dynamically changing affect throughout the tutoring session. In this thesis, I explore the feasibility of building an Affective Tutoring System (ATS) which senses the student’s affect on a moment-to-moment basis with the use of unobtrusive sensors in the context of computer programming tutoring. The novel use of keystrokes and mouse clicks for affect sensing is proposed here as they are ubiquitous and unobtrusive. I first establish the viability of using keystrokes and contextual logs for affect sensing first on a per exercise session level and then on a more granular basis of 30 seconds. Subsequently, I move on to investigate the use of multiple sensing channels e.g. facial, keystrokes, mouse clicks, contextual logs and head postures to enhance the availability and accuracy of sensing. The results indicated that it is viable to use keystrokes for affect sensing. In addition, the combination of multiple sensor modes enhances the accuracy of affect sensing. From the results, the sensor modes that are most significant for affect sensing are the head postures and facial modes. Nevertheless, keystrokes make up for the periods of unavailability of the former. With the affect sensing (both sensing of frustration and disengagement) in place, I moved on to architect and design the ATS and conducted an experimental study and a series of focus group discussions to evaluate the ATS. The results showed that the ATS is rated positively by the participants for usability and acceptance. The ATS is also effective in enhancing the learning of the studentsNanyang Polytechni

Investor sentiment in blogs : design of a classifier and validation by a portfolio simulation

How can investment recommendations available on the web significantly improve stock selection? This dissertation shows how online investment recommendations can automatically be analyzed, aggregated, and used to achieve a return above the markets. To this respect, it is crucial to understand how investment recommendations affect returns. Therefore, the dissertation examines the effects of direct and indirect investment recommendations from blogs in the form of investor sentiments (i.e., opinions) on the expected development of stock prices. Blogs have made it possible for everyone to publish articles on the web. The studied blog platforms Seekingalpha and Blogspot host a wealth of semi-professional stock analyses, investor opinions, company rumors, and stock recommendations. The dissertations study uses about 77,000 articles from Seekingalpha and about 198,000 articles from Blogspot over a five-year period (2007-2011). A novel text classification method is developed for the automatic classification of blog articles in a positive vs. negative sentiment. To achieve a high classification accuracy, experiments were carried out to configure this method. The text classification method uses machine learning techniques, which learn from manually classified articles from a novel corpus. Using behavioral finance theory, hypotheses are developed about the effects of investor sentiments on a portfolios returns. To test these hypotheses, a monthly selection of stocks of the Dow Jones Industrial Average into a portfolio was simulated (i.e., backtested). The selection is made by means of the ranking of the monthly aggregated overall sentiment of all articles regarding a specific stock. The results show that a return above the markets can be achieved with aggregated investor sentiments from the Seekingalpha platform. In most cases, the achieved return exceeds the return of a momentum portfolio based solely on past returns. For the platform Blogspot, results are weaker. Overall, it seems advisable for investors to select a small number of stocks based on the most positive and most negative monthly investor sentiments from professional blogs.Wie können Anlageempfehlungen aus dem Web die Aktienauswahl deutlich verbessern? Die Dissertation zeigt, wie solche Online-Anlageempfehlungen automatisch analysiert, verdichtet und zur Erzielung einer über dem Markt liegenden Rendite genutzt werden können. Hierzu ist entscheidend, wie sich Anlageempfehlungen auf die Renditen auswirken. Daher untersucht die Dissertation die Wirkung von direkten und indirekten Anlageempfehlungen aus Blogs in der Form von Meinungen (sogenannte Investor Sentiments) zur erwarteten Entwicklung von Aktienkursen. Mit Hilfe von Blogs ist es jedermann möglich, im Web Artikel zu veröffentlichen. Auf den untersuchten Blog-Plattformen Seekingalpha und Blogspot finden sich zahllose semi-professionelle Aktienanalysen, Anlegermeinungen, Gerüchte zu Unternehmen und Aktienempfehlungen. Für die Untersuchung verwendet die Arbeit etwa 77.000 Artikel von Seekingalpha und etwa 198.000 Artikel von Blogspot aus einem Fünfjahreszeitraum (2007-2011). Für die automatische Einstufung der Artikel in eine positive vs. negative Meinung wird eine neuartige Textklassifikationsmethode entworfen. Um eine möglichst hohe Genauigkeit der Methode zu erzielen, wurden Experimente zur Parametrisierung durchgeführt. Die Textklassifikation erfolgte mit Hilfe eines maschinellen Lernverfahrens. Das Verfahren lernt mit Hilfe von manuell eingestuften Artikeln aus einem eigens entwickelten Korpus. Unter Bezugnahme auf die Behavioral-Finance-Theorie werden Hypothesen zu den Wirkungen von Anlegermeinungen auf Renditen eines Portfolios entwickelt. Zur Überprüfung der Hypothesen wurde eine monatliche Auswahl von Aktien des Dow Jones Industrial Average in ein Portfolio über einen Fünfjahreszeitraum simuliert. Die Auswahl erfolgt mit Hilfe der Rangfolge der monatlich verdichteten Gesamtmeinung aller Artikel zu einer Aktie. Die Ergebnisse zeigen, dass mit Hilfe der verdichteten Anlegermeinungen von der Plattform Seekingalpha eine über dem Markt liegende Rendite erzielt werden kann. Die Rendite übertrifft in fast allen Fällen die Rendite eines Portfolios, das rein basierend auf vergangenen Renditen erstellt wurde. Für die Plattform Blogspot fallen die Ergebnisse schwächer aus. Insgesamt scheint es für Anleger empfehlenswert, eine kleine Anzahl von Aktien entsprechend der positivsten und negativsten monatlich verdichteten Anlegermeinung aus professionellen Blogs auszuwählen