Mimicking the interlocutor’s speech: models of speaker entrainment for improving the naturalness of spoken dialogue systems

A medida que las tecnologías de procesamiento del habla continúan mejorando, gradualmente nos acercamos al viejo sueño de crear una máquina que hable. Los actuales sistemas interactivos de diálogo hablado permiten que los usuarios realicen tareas simples, tales como transacciones bancarias y reservas en hoteles, mediante la interacción verbal. Pese a ser relativamente exitosas, estas conversaciones humanocomputadora aún tienen un largo camino para recorrer en cuanto a su naturalidad: estos sistemas tienden a ser descriptos por los usuarios como “extraños” o incluso “intimidantes”. Entre las razones principales para esta falta de naturalidad, figura el modelado imperfecto de la variación prosódica, o cómo algunas propiedades del habla (tales como la entonación, la intensidad o el ritmo) cambian en las expresiones verbales. Los sistemas actuales todavía son incapaces de manejar estas características en forma correcta, tanto al entender el habla del usuario como para producir respuestas sintetizadas. La variación prosódica es extremadamente compleja en el habla espontánea, y se sabe que la afectan varios niveles de representación lingüística (léxica, sintáctica, semántica y pragmática). En el presente artículo, enfocamos nuestra atención en una dimensión particular de variación prosódica, conocida como “mimetización entre interlocutores”, que consiste en la alineación automática de características del habla entre los participantes de un diálogo. Tras un repaso general de la literatura de estos temas, describimos un proyecto de investigación en curso que busca modelar la mimetización prosódica en diálogos.As speech processing technologies continue to improve, the old dream of creating a machine that talks gradually becomes real. The present interactive speech systems enable users to perform simple tasks such as banking transactions and hotel reservations, through verbal interaction. Despite being relatively successful, these human-computer conversations still have a long way to go regarding their naturalness: these systems tend to be described as “odd” or even “intimidating” by users. Among the main reasons for this lack of naturalness, is the flawed modeling of prosodic variation or the way some properties of speech (such as intonation, intensity and rhythm) change in verbal expressions. Current systems are still unable to handle these features correctly, both to understand the speech of the user as to produce synthesized responses. Prosodic variation is extremely complex in spontaneous speech, and it is well known that it´s affected by several levels of linguistic representation (lexical, syntactic, semantic and pragmatic). The present article focuses on a specific dimension of prosodic variation, known as “mimetization between interlocutors”, which consists in the automatic alignment of speech features between the participants of a dialogue. After a general overview of the literature on these subjects, a research project in process that seeks to model the prosodic mimetizatin in dialogues is described.Fil: Gravano, Agustin. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

You Can Be Me!

Dr. Gravano discusses becomes a physical therapist

Residency Corner

The new Residency Corner will feature articles written by residents that describe topics such as in-depth case analysis, problem solving, and practice as nonphysician primary care providers. Here residents will get to discuss their findings and thought-processes with an audience of their peers--those clinicians and academics interested in the advancement of geriatric health care. Also, for those who may be interested to see what geriatric residency education is really all about, these clinical snapshots can help remove the veil and provide a glimpse into the level of practice our residents are experiencing. Yet, this is only one half of the equation. What’s missing? The Residency Director, Coordinator, and Faculty points of view. While residents have been sounding off on their accomplishments and challenges, we would like to extend the offer to current residency directors to chronicle their successes and offer pearls of knowledge to anyone interested in starting a residency program

Career Paths, Barriers, and Professional Experiences: A Comparison Study of Physical Therapy Program Directors in the United States

The entry-level degree for physical therapists today is a professional doctoral degree. It is similar to a medical degree program where the expected outcome of its graduates is clinical practice, and there is no formal training in academic teaching or administration. The purpose of this study was to determine the leadership characteristics, styles, and behaviors of accredited Doctor of Physical Therapy (DPT) program directors (PD) and to gain a better understanding of their preparation and path to program leadership. The survey instrument was sent electronically to all program directors of accredited DPT programs in the United States (N=233) and the response rate was 46%. The results of this study are presented as comparisons between PT Program Directors with DPT degrees and those with other academic terminal doctoral (OATD) degrees. Survey items included both qualitative and quantitative questions and analyzed for similarities, trends, and patterns between the two groups. The data were analyzed using descriptive statistics, t-tests, and chi square calculations. Significant differences were found between groups regarding leadership training, style, and characteristics. When asked about years of experience, overall, most DPT program directors were in their first DPT PD position and had between 3-9 years experience as a PD. Most subjects reported feeling moderately to wellprepared for their first role as PD, crediting the most useful primary means of preparation after earning their entry-level PT degree as (1) on the job training (79%), (2) completing an advanced degree (69%), (3) seeking a mentor (60%), and (4) leadership training from their institution (41%). Both groups reported the same four primary methods in the same order. Similarly, the method of leadership preparation of current PDs with a DPT in their role as PD did not differ significantly at the specified .05 level (x 2 = 1.54, df = 4, p = .672). The majority of PDs completed additional training beyond their entry-level PT degree in the topics of (1) conflict resolution, (2) communication, and (3) leadership in order to help prepare for their first role as PD. Most (84%) program directors reported that their preferred leadership styles were Servant leadership and/or Transformational leadership. A significant difference was found in the preference for the transformational style for the DPT group at the .01 level (z = 3.2137, p = .0013). Conversely, no significant difference was found for the OATD group’s preference of servant leadership (z = .5553, p = .5754). Leadership characteristics differed significantly between groups. The DPT group favored the characteristics of empowering and respect significantly higher than the OATD group. The OATD group favored knowledge and confidence significantly higher than the DPT group. No significant differences were found between groups regarding self-reported personal satisfaction in the role of PD and future career aspirations. The chi-square analysis found no association between the independent variables of highest PT degree earned and future career aspirations of all PDs (x2 = 5.79, df = 3, p = .2152) Both groups had similar plans for the next five years, with a combined total of 67% of current PDs planning to leave their post as PD, indicating a significant number of vacancies in the near future. Leadership training programs are essential for physical therapy faculty and clinicians who are considering a shift from clinical practice or teaching into administration. The survey findings suggest that there is a growing need to train and develop current PT practitioners and faculty members to fill the role of PT program director. As the entry-level degree of physical therapy has evolved quickly in the last twenty-five years from master’s to doctoral degree, qualified leaders and faculty are needed more than ever. Similarly, new DPT programs are opening frequently, and the demand for capable program directors is increasing. The majority of current PT PDs agree that securing a mentor and building a strong support network are key components of leadership development and should be an integral part of a leadership-training program. The results of this study suggest that formal training programs such as the American Physical Therapy Association’s Educational Leadership Institute Fellowship program, as well as advanced master’s and academic doctoral degrees that emphasize higher education administration, are valuable resources for leadership training. A well-defined, ongoing, and specific training program for future leaders, which builds on and leads to effective leadership behaviors and characteristics may be a potential solution to an impending leadership crisis in PT education. The results of this survey clarify the leadership training and career paths of current DPT program directors, and they identify the leadership characteristics and behaviors needed to lead a professional educational program in a unique and dynamic environment. These findings add to the growing body of knowledge of how to best prepare leaders for the future of PT education

Classification-Aware Hidden-Web Text Database Selection,

Many valuable text databases on the web have noncrawlable contents that are “hidden” behind search interfaces. Metasearchers are helpful tools for searching over multiple such “hidden-web” text databases at once through a unified query interface. An important step in the metasearching process is database selection, or determining which databases are the most relevant for a given user query. The state-of-the-art database selection techniques rely on statistical summaries of the database contents, generally including the database vocabulary and associated word frequencies. Unfortunately, hidden-web text databases typically do not export such summaries, so previous research has developed algorithms for constructing approximate content summaries from document samples extracted from the databases via querying.We present a novel “focused-probing” sampling algorithm that detects the topics covered in a database and adaptively extracts documents that are representative of the topic coverage of the database. Our algorithm is the first to construct content summaries that include the frequencies of the words in the database. Unfortunately, Zipf’s law practically guarantees that for any relatively large database, content summaries built from moderately sized document samples will fail to cover many low-frequency words; in turn, incomplete content summaries might negatively affect the database selection process, especially for short queries with infrequent words. To enhance the sparse document samples and improve the database selection decisions, we exploit the fact that topically similar databases tend to have similar vocabularies, so samples extracted from databases with a similar topical focus can complement each other. We have developed two database selection algorithms that exploit this observation. The first algorithm proceeds hierarchically and selects the best categories for a query, and then sends the query to the appropriate databases in the chosen categories. The second algorithm uses “shrinkage,” a statistical technique for improving parameter estimation in the face of sparse data, to enhance the database content summaries with category-specific words.We describe how to modify existing database selection algorithms to adaptively decide (at runtime) whether shrinkage is beneficial for a query. A thorough evaluation over a variety of databases, including 315 real web databases as well as TREC data, suggests that the proposed sampling methods generate high-quality content summaries and that the database selection algorithms produce significantly more relevant database selection decisions and overall search results than existing algorithms.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Extracting Relations from Large Plain-Text Collections

Text documents often contain valuable structured data that is hidden in regular English sentences. This data is best exploited if available as a relational table that we could use for answering precise queriesor for running data mining tasks. We explore a technique for extracting such tables from document collections that requires only a handful of training examples from users. These examples are used to generate extraction patterns,that in turn result in new tuples being extracted from the document collection. We build on this idea and present our Snowball system. Snowball introduces novel strategies for generating patterns and extracting tuples from plain-text documents. At each iteration of the extraction process, Snowball evaluates the quality of these patterns and tuples without human intervention,In this paper we also develop a scalable evaluation methodology and metrics for our task, and present a thorough experimental evaluation of Snowball and comparable techniques over a collection of more than 300,000 newspaper documents

Summarizing and Searching Hidden-Web Databases Hierarchically Using Focused Probes

Many valuable text databases on the web have non-crawlable contents that are "hidden" behind search interfaces. Metasearchers are helpful tools for searching over many such databases at once through a unified query interface. A critical task for a metasearcher to process a query efficiently and effectively is the selection of the most promising databases for the query, a task that typically relies on statistical summaries of the database contents. Unfortunately, web-accessible text databases do not generally export content summaries. In this paper, we present an algorithm to derive content summaries from "uncooperative" databases by using "focused query probes," which adaptively zoom in on and extract documents that are representative of the topic coverage of the databases. The content summaries that result from this algorithm are efficient to derive and more accurate than those from previously proposed probing techniques for content-summary extraction. We also present a novel database selection algorithm that exploits both the extracted content summaries and a hierarchical classification of the databases, automatically derived during probing, to produce accurate results even for imperfect content summaries. Finally, we evaluate our techniques thoroughly using a variety of databases, including 50 real web-accessible text databases

Rapid Language Model Development Using External Resources for New Spoken Dialog Domains

This paper addresses a critical problem in deploying a spoken dialog system (SDS). One of the main bottlenecks of SDS deployment for a new domain is data sparseness in building a statistical language model. Our goal is to devise a method to efficiently build a reliable language model for a new SDS. We consider the worst yet quite common scenario where only a small amount (∼1.7K utterances) of domain specific data is available for the target domain. We present a new method that exploits external static text resources that are collected for other speech recognition tasks as well as dynamic text resources acquired from World Wide Web (WWW). We show that language models built using external resources can jointly be used with limited in–domain (baseline) language model to obtain significant improvements in speech recognition accuracy. Combining language models built using external resources with the in–domain language model provides over 20 % reduction in WER over the baseline in–domain language model. Equivalently, we achieve almost the same level of performance by having ten times as much in–domain data (17K utterances)