Under severe channel mismatch conditions, such as training with far-field speech and testing with telephone data, performance of speaker identification (SID) degrades significantly, often below practical use. But for many SID tasks, it is sufficient to recognize an N-best list of speakers for further human analysis. We investigate N-best SID accuracy for matched (telephone/telephone) and mismatched (far-field/telephone) train/test channel conditions. Using an SVM-GMM supervector (GSV), pitch and formant frequency histograms (PFH) and cross-channel adaptation using cohorts, we reduced matched channel error rate by over 25% relative to the baseline (GMM-UBM), for top-1, and achieved mismatched N-best accuracy comparable to the baseline.

Progress bars are prevalent in modern user interfaces. Typically, a linear function is employed such that the progress of the bar is directly proportional to how much work has been completed. However, numerous factors cause progress bars to proceed at non-linear rates. Additionally, humans perceive time in a non-linear way. This paper explores the impact of various progress bar behaviors on user perception of process duration. The results are used to suggest several design considerations that can make progress bars appear faster and ultimately improve users' computing experience.

Despite its widespread use, voicemail presents numerous usability challenges: People must listen to messages in their entirety, they cannot search by keywords, and audio files do not naturally support visual skimming. SCANMail overcomes these flaws by automatically generating text transcripts of voicemail messages and presenting them in an email-like interface. Transcripts facilitate quick browsing and permanent archive. However, errors from the automatic speech recognition (ASR) hinder the usefulness of the transcripts. The work presented here specifically addresses these problems by evaluating user-initiated error correction of transcripts. User studies of two editor interfaces-a grammar-assisted menu and simple replacement by typing-reveal reduced audio playback times and an emphasis on editing important words with the menu, suggesting its value in mobile environments where limited input capabilities are the norm and user privacy is essential. The study also adds to the scarce body of work on ASR confidence shading, suggesting that shading may be more helpful than previously reported.

Editing speech data is currently time-consuming and error-prone. Speech editors rely on acoustic waveform representations, which force users to repeatedly sample the underlying speech to identify words and phrases to edit. Instead we developed a semantic editor that reduces the need for extensive sampling by providing access to meaning. The editor shows a time-aligned errorful transcript produced by applying automatic speech recognition (ASR) to the original speech. Users visually scan the words in the transcript to identify important phrases. They then edit the transcript directly using standard word processing 'cut and paste' operations, which extract the corresponding time-aligned speech. ASR errors mean that users must supplement what they read in the transcript by accessing the original speech. Even when there are transcript errors, however, the semantic representation still provides users with enough information to target what they edit and play, reducing the need for extensive sampling. A laboratory evaluation showed that semantic editing is more efficient than acoustic editing even when ASR is highly inaccurate.

Social data mining systemsenable people to share opinions and benefit from each other's experience. They do this by mining and redistributing information from computational records of social activity such as Usenet messages, system usage history, citations, or hyperlinks. Some general questions for evaluating such systems are: (1) is the extracted information valuable? and (2) do interfaces based on the information improve user task performance? We report here onTopicShop, a system that mines information from the structure and content of Web pages and provides an exploratory information workspace interface. We carried out experiments that yielded positive answers to both evaluation questions. First, a number of automatically computable features about Web sites do a good job of predicting expert quality judgments about sites. Second, compared to popular Web search interfaces, the TopicShop interface to this information lets users select significantly more high-quality sites, in less time and with less effort, and to organize the sites they select into personally meaningful collections more quickly and easily. We conclude by discussing how our results may be applied and considering how they touch on general issues concerning quality, expertise, and consensus.

Many applications require users to specify preferences. We support users in this task by letting them define preferences relative to their personal history or that of other users. We implement this idea using a graphical technique called control shadows, which we have implemented on both a desktop computer and on a cell phone with a small, grayscale display. An empirical study compared user performance on the graphical interface and a text table interface with identical functionality. On the desktop, users completed their tasks more quickly and effectively and strongly preferred the graphical interface. On the cell phone, there was no significant difference between the graphical and table interfaces. Finally, personal history proved useful in specifying preferences, but history of other users was not helpful

Increasing amounts of public, corporate, and private speech data are now available on-line. These are limited in their usefulness, however, by the lack of tools to permit their browsing and search. The goal of our research is to provide tools to overcome the inherent difficulties of speech access, by supporting visual scanning, search, and information extraction. We describe a novel principle for the design of UIs to speech data: What You See Is Almost What You Hear (WYSIAWYH). In WYSIAWYH, automatic speech recognition (ASR) generates a transcript of the speech data. The transcript is then used as a visual analogue to that underlying data. A graphical user interface allows users to visually scan, read, annotate and search these transcripts. Users can also use the transcript to access and play specific regions of the underlying message. We first summarize previous studies of voicemail usage that motivated the WYSIAWYH principle, and describe a voicemail UI, SCANMail, that embodies WYSIAWYH. We report on a laboratory experiment and a two-month field trial evaluation. SCANMail outperformed a state of the art voicemail system on core voicemail tasks. This was attributable to SCANMail's support for visual scanning, search and information extraction. While the ASR transcripts contain errors, they nevertheless improve the efficiency of voicemail processing. Transcripts either provide enough information for users to extract key points or to navigate to important regions of the underlying speech, which they can then play directly

For many topics, the World Wide Web contains hundreds or thousands of relevant documents of widely varying quality. Users face a daunting challenge in identifying a small subset of documents worthy of their attention.

Article TopicShop: enhanced support for evaluating and organizing collections of Web sites Share on Authors: Brian Amento AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJ and Department of Computer Science, Virginia Tech, Blacksburg, VA AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJ and Department of Computer Science, Virginia Tech, Blacksburg, VAView Profile , Loren Terveen AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJ AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJView Profile , Will Hill AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJ AT&T Labs - Research, 180 Park Avenue, P.O. Box 971, Florham Park, NJView Profile , Deborah Hix Department of Computer Science, Virginia Tech, Blacksburg, VA Department of Computer Science, Virginia Tech, Blacksburg, VAView Profile Authors Info & Claims UIST '00: Proceedings of the 13th annual ACM symposium on User interface software and technologyNovember 2000 Pages 201–209https://doi.org/10.1145/354401.354771Online:01 November 2000Publication History 19citation728DownloadsMetricsTotal Citations19Total Downloads728Last 12 Months21Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Topic management is the task of gathering, evaluating, organizing, and sharing a set of web sites for a specific topic. Current web tools do not provide adequate support for this task. We created the TopicShop system to address this need. TopicShop includes (1) a webcrawler that discovers relevant web sites and builds site profiles, and (2) user interfaces for exploring and organizing sites. We conducted an empirical study comparing user performance with TopicShop vs. YahooTM. TopicShop subjects found over 80% more high-quality sites (where quality was determined by independent expert judgements) while browsing only 8 1% as many sites and completing their task in 89% of the time. The site profile data that TopicShop provides - in particular, the number of pages on a site and the number of other sites that link to it - was the key to these results, as users exploited it to identify the most promising sites quickly and easily.

For many purposes, the Web page is too small a unit of interaction and analysis. Web sites are structured multimedia documents consisting of many pages, and users often are interested in obtaining and evaluating entire collections of topically related sites. Once such a collection is obtained, users face the challenge of exploring, comprehending and organizing the items. We report four innovations that address these user needs: (1) we replaced the Web page with the Websiteas the basic unit of interaction and analysis;(2) we defined a new informationstructure, theclan graph, that groups together sets of related sites; (3) we augment the representation of a site with asite profile, information about site structure and content that helps inform user evaluation of a site; and (4) we invented a new graph visualization, theauditorium visualization, that reveals important structural and content properties of sites within a clan graph. Detailed analysis and user studies document the utility of this approach. The clan graph construction algorithm tends to filter out irrelevant sites and discover additional relevant items. The auditorium visualization, augmented with drill-down capabilities to explore site profile data, helps users to find high-quality sites as well as sites that serve a particular function.

As people participate in the thousands of global conversations that comprise Usenet news, one thing they do is post their opinions of web resources. Phoaks is a collaborative filtering system that continuously parses, classifies, abstracts and tallies those opinions. About 3,500 users per day consult Phoaks web pages that reflect the results. Phoaks also features a general architecture for building similar collaborative filtering interfaces to conversational data. We report here on the Phoaks resource recommendation interface, the architecture, and the issues and experience that make up its rationale. Keywords human-computer interaction, human interface, computersupported cooperative work, organizational computing, social filtering, collaborative filtering, data mining,