We propose a Laplacian structured sparsity model to study computational branding analytics.To do this, we collected customer reviews from Starbucks, Dunkin' Donuts, and other coffee shops across 38 major cities in the Midwest and Northeastern regions of USA.We study the brand related language use through these reviews, with focuses on the brand satisfaction and gender factors.In particular, we perform three tasks: automatic brand identification from raw text, joint brand-satisfaction prediction, and joint brandgender-satisfaction prediction.This work extends previous studies in text classification by incorporating the dependency and interaction among local features in the form of structured sparsity in a log-linear model.Our quantitative evaluation shows that our approach which combines the advantages of graphical modeling and sparsity modeling techniques significantly outperforms various standard and stateof-the-art text classification algorithms.In addition, qualitative analysis of our model reveals important features of the language uses associated with the specific brands.

This paper introduces a new optimization technique for moving segment labels (phone and subphonetic) to optimize statistical parametric speech synthesis models. The choice of objective measures is investigated thoroughly and listening tests show the results to significantly improve the quality of the generated speech equivalent to increasing the database size by 3 fold.

This paper studies two ways for helping non-specialist users develop speech systems from limited data for new languages. Focused web re-crawling finds additional examples of text matching the domain as specified by the user. This improves the language model and cuts word error rate nearly in half. Iterative voice building with interleaved lexicon construction uses the voice from a previous iteration to help construct an improved voice. 4.5 hours of the user’s time reduces transcription error rate from 32 % to 4%. 1.

In this paper we describe the design and implementation of a user interface for SPICE, a web-based toolkit for rapid prototyping of speech and language processing components.We report on the challenges and experiences gathered from testing these tools in an advanced graduate hands-on course, in which we created speech recognition, speech synthesis, and smalldomain translation components for 10 different languages within only 6 weeks.

Subphonetic discovery through segmental clustering is a central step in building a corpus-based synthesizer. To help decide what clustering algorithm to use we employed mergeand-split tests on English fricatives. Compared to reference of 2%, Gaussian EM achieved a misclassification rate of 6%, Kmeans 10%, while predictive CART trees performed poorly.

In CMU's Blizzard Challenge 2005 entry we investigated twelve ideas for improving Festival-based unit selection voices. We tracked progress by adopting a 3-tiered strategy in which candidate ideas must pass through three stages of listening tests to warrant inclusion in the final build. This allowed us to evaluate ideas consistently without us having large human resources at our disposal, and thereby improve upon our baseline system within a short amount of time. 1.

For segmenting a speech database, using a family of acoustic models provides multiple estimates of each boundary point. This is more robust than a single estimate because by taking consensus values, large labeling errors are less prevalent in the synthesis catalog, which improves the resulting voice. This paper describes HMM-based segmentation in which up to 500 related models are applied to each wavefile. In a listening test of twelve utterances, human judges preferred the proposed technique over the baseline by a tally of 6 to 2, with 4 ties.

As part of improved support for building unit selection voices, the Festival speech synthesis system now includes two algorithms for automatic labeling of wavefile data. The two methods are based on dynamic time warping and HMM-based acoustic modeling. Our experiments show that DTW is more accurate 70% of the time, but is also more prone to gross labeling errors. HMM modeling exhibits a systematic bias of 15 ms. Combining both methods directs human labelers towards data most likely to be problematic.

A meeting browser is a system that allows users to review a multimedia meeting record from a variety of indexing methods. Identification of meeting participants is essential for creating such a multimedia meeting record. Moreover, knowing who is speaking can enhance the performance of speech recognition and indexing meeting transcription. In this paper, we present an approach that identifies meeting participants by fusing multimodal inputs. We use face ID, speaker ID, color appearance ID, and sound source directional ID to identify and track meeting. After describing the different modules in detail, we will discuss a framework for combining the information sources. Integration of the multimodal people ID into the multimedia meeting browser is in its preliminary stage.

Multimedia information retrieval is a challenging problem because multimedia information is not inherently structured.Jabber is an experimental system that attempts to bring some structure to this task.Jabber allows users to retrieve records of videoconferences based upon the concepts discussed.In this paper we introduce ConceptFinder, a sub-system within Jabber, and show how it is able to process the spoken text of a meeting into meeting topics.Con-ceptFinder can make subtle distinctions among different senses of the same words, and is able to summarize a set of related words, giving a name to each topic.Users can then use this name to query or browse the stored multimedia, through Jabber's user interface.By presenting information that closely matches a user's expectations, the challenge of multimedia retrieval is rendered more tractable.

Fractal image compression, despite its great potential, suffers from some flaws that may prevent its adaptation from becoming more widespread. One such problem is the difficulty of guaranteeing convergence, let alone a specific error tolerance. To help surmount this problem, we have introduced the terms compound, cycle, and partial contractivity concepts indispensable for understanding convergence of fractal images. Most important, they connect the behavior of individual pixels to the image as a whole, and relate such behavior to the component affine transforms.