Teenage and adult speech in school context: building and processing a corpus of European Portuguese

We present a corpus of European Portuguese spoken by teenagers and adults in school context, CPE-FACES, with an overview of the differential characteristics of high school oral presentations and the challenges this data poses to automatic speech processing. The CPE-FACES corpus has been created with two main goals: to provide a resource for the study of prosodic patterns in both spontaneous and prepared unscripted speech, and to capture inter-speaker and speaking style variations common at school, for research on oral presentations. Research on speaking styles is still largely based on adult speech. References to teenagers are sparse and cross-analyses of speech types comparing teenagers and adults are rare. We expect CPE-FACES, currently a unique resource in this domain, will contribute to filling this gap in European Portuguese. Focusing on disfluencies and phrase-final phonetic-phonological processes we show the impact of teenage speech on the automatic segmentation of oral presentations. Analyzing fluent final intonation contours in declarative utterances, we also show that communicative situation specificities, speaker status and cross-gender differences are key factors in speaking style variation at school.info:eu-repo/semantics/acceptedVersio

Teenage and Adult Speech in School Context: Building and Processing a Corpus of European Portuguese

We present a corpus of European Portuguese spoken by teenagers and adults in school context, CPE-FACES, with an overview of the differential characteristics of high school oral presentations and the challenges this data poses to automatic speech processing. The CPE-FACES corpus has been created with two main goals: to provide a resource for the study of prosodic patterns in both spontaneous and prepared unscripted speech, and to capture inter-speaker and speaking style variations common at school, for research on oral presentations. Research on speaking styles is still largely based on adult speech. References to teenagers are sparse and cross-analyses of speech types comparing teenagers and adults are rare. We expect CPE-FACES, currently a unique resource in this domain, will contribute to filling this gap in European Portuguese. Focusing on disfluencies and phrase-final phonetic-phonological processes we show the impact of teenage speech on the automatic segmentation of oral presentations. Analyzing fluent final intonation contours in declarative utterances, we also show that communicative situation specificities, speaker status and cross gender differences are key factors in speaking style variation at school.info:eu-repo/semantics/publishedVersio

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

Verb similarity: comparing corpus and psycholinguistic data

Similarity, which plays a key role in fields like cognitive science, psycholinguistics and natural language processing, is a broad and multifaceted concept. In this work we analyse how two approaches that belong to different perspectives, the corpus view and the psycholinguistic view, articulate similarity between verb senses in Spanish. Specifically, we compare the similarity between verb senses based on their argument structure, which is captured through semantic roles, with their similarity defined by word associations. We address the question of whether verb argument structure, which reflects the expression of the events, and word associations, which are related to the speakers' organization of the mental lexicon, shape similarity between verbs in a congruent manner, a topic which has not been explored previously. While we find significant correlations between verb sense similarities obtained from these two approaches, our findings also highlight some discrepancies between them and the importance of the degree of abstraction of the corpus annotation and psycholinguistic representations.La similitud, que desempeña un papel clave en campos como la ciencia cognitiva, la psicolingüística y el procesamiento del lenguaje natural, es un concepto amplio y multifacético. En este trabajo analizamos cómo dos enfoques que pertenecen a diferentes perspectivas, la visión del corpus y la visión psicolingüística, articulan la semejanza entre los sentidos verbales en español. Específicamente, comparamos la similitud entre los sentidos verbales basados en su estructura argumental, que se capta a través de roles semánticos, con su similitud definida por las asociaciones de palabras. Abordamos la cuestión de si la estructura del argumento verbal, que refleja la expresión de los acontecimientos, y las asociaciones de palabras, que están relacionadas con la organización de los hablantes del léxico mental, forman similitud entre los verbos de una manera congruente, un tema que no ha sido explorado previamente. Mientras que encontramos correlaciones significativas entre las similitudes de los sentidos verbales obtenidas de estos dos enfoques, nuestros hallazgos también resaltan algunas discrepancias entre ellos y la importancia del grado de abstracción de la anotación del corpus y las representaciones psicolingüísticas.La similitud, que exerceix un paper clau en camps com la ciència cognitiva, la psicolingüística i el processament del llenguatge natural, és un concepte ampli i multifacètic. En aquest treball analitzem com dos enfocaments que pertanyen a diferents perspectives, la visió del corpus i la visió psicolingüística, articulen la semblança entre els sentits verbals en espanyol. Específicament, comparem la similitud entre els sentits verbals basats en la seva estructura argumental, que es capta a través de rols semàntics, amb la seva similitud definida per les associacions de paraules. Abordem la qüestió de si l'estructura de l'argument verbal, que reflecteix l'expressió dels esdeveniments, i les associacions de paraules, que estan relacionades amb l'organització dels parlants del lèxic mental, formen similitud entre els verbs d'una manera congruent, un tema que no ha estat explorat prèviament. Mentre que trobem correlacions significatives entre les similituds dels sentits verbals obtingudes d'aquests dos enfocaments, les nostres troballes també ressalten algunes discrepàncies entre ells i la importància del grau d'abstracció de l'anotació del corpus i les representacions psicolingüístiques

A Computational Theory of the Use-Mention Distinction in Natural Language

To understand the language we use, we sometimes must turn language on itself, and we do this through an understanding of the use-mention distinction. In particular, we are able to recognize mentioned language: that is, tokens (e.g., words, phrases, sentences, letters, symbols, sounds) produced to draw attention to linguistic properties that they possess. Evidence suggests that humans frequently employ the use-mention distinction, and we would be severely handicapped without it; mentioned language frequently occurs for the introduction of new words, attribution of statements, explanation of meaning, and assignment of names. Moreover, just as we benefit from mutual recognition of the use-mention distinction, the potential exists for us to benefit from language technologies that recognize it as well. With a better understanding of the use-mention distinction, applications can be built to extract valuable information from mentioned language, leading to better language learning materials, precise dictionary building tools, and highly adaptive computer dialogue systems. This dissertation presents the first computational study of how the use-mention distinction occurs in natural language, with a focus on occurrences of mentioned language. Three specific contributions are made. The first is a framework for identifying and analyzing instances of mentioned language, in an effort to reconcile elements of previous theoretical work for practical use. Definitions for mentioned language, metalanguage, and quotation have been formulated, and a procedural rubric has been constructed for labeling instances of mentioned language. The second is a sequence of three labeled corpora of mentioned language, containing delineated instances of the phenomenon. The corpora illustrate the variety of mentioned language, and they enable analysis of how the phenomenon relates to sentence structure. Using these corpora, inter-annotator agreement studies have quantified the concurrence of human readers in labeling the phenomenon. The third contribution is a method for identifying common forms of mentioned language in text, using patterns in metalanguage and sentence structure. Although the full breadth of the phenomenon is likely to elude computational tools for the foreseeable future, some specific, common rules for detecting and delineating mentioned language have been shown to perform well

Generating Tailored, Comparative Descriptions with Contextually Appropriate Intonation

Generating responses that take user preferences into account requires adaptation at all levels of the generation process. This article describes a multi-level approach to presenting user-tailored information in spoken dialogues which brings together for the first time multi-attribute decision models, strategic content planning, surface realization that incorporates prosody prediction, and unit selection synthesis that takes the resulting prosodic structure into account. The system selects the most important options to mention and the attributes that are most relevant to choosing between them, based on the user model. Multiple options are selected when each offers a compelling trade-off. To convey these trade-offs, the system employs a novel presentation strategy which straightforwardly lends itself to the determination of information structure, as well as the contents of referring expressions. During surface realization, the prosodic structure is derived from the information structure using Combinatory Categorial Grammar in a way that allows phrase boundaries to be determined in a flexible, data-driven fashion. This approach to choosing pitch accents and edge tones is shown to yield prosodic structures with significantly higher acceptability than baseline prosody prediction models in an expert evaluation. These prosodic structures are then shown to enable perceptibly more natural synthesis using a unit selection voice that aims to produce the target tunes, in comparison to two baseline synthetic voices. An expert evaluation and f0 analysis confirm the superiority of the generator-driven intonation and its contribution to listeners' ratings