A proposal for the development of adaptive spoken interfaces to access the Web

Spoken dialog systems have been proposed as a solution to facilitate a more natural human–machine interaction. In this paper, we propose a framework to model the user׳s intention during the dialog and adapt the dialog model dynamically to the user needs and preferences, thus developing more efficient, adapted, and usable spoken dialog systems. Our framework employs statistical models based on neural networks that take into account the history of the dialog up to the current dialog state in order to predict the user׳s intention and the next system response. We describe our proposal and detail its application in the Let׳s Go spoken dialog system.Work partially supported by Projects MINECO TEC2012-37832- C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/ TIC-1485

An evaluation paradigm for spoken dialog systems based on crowdsourcing and collaborative filtering.

Yang, Zhaojun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 92-99).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- SDS Architecture --- p.1Chapter 1.2 --- Dialog Model --- p.3Chapter 1.3 --- SDS Evaluation --- p.4Chapter 1.4 --- Thesis Outline --- p.7Chapter 2 --- Previous Work --- p.9Chapter 2.1 --- Approaches to Dialog Modeling --- p.9Chapter 2.1.1 --- Handcrafted Dialog Modeling --- p.9Chapter 2.1.2 --- Statistical Dialog Modeling --- p.12Chapter 2.2 --- Evaluation Metrics --- p.16Chapter 2.2.1 --- Subjective User Judgments --- p.17Chapter 2.2.2 --- Interaction Metrics --- p.18Chapter 2.3 --- The PARADISE Framework --- p.19Chapter 2.4 --- Chapter Summary --- p.22Chapter 3 --- Implementation of a Dialog System based on POMDP --- p.23Chapter 3.1 --- Partially Observable Markov Decision Processes (POMDPs) --- p.24Chapter 3.1.1 --- Formal Definition --- p.24Chapter 3.1.2 --- Value Iteration --- p.26Chapter 3.1.3 --- Point-based Value Iteration --- p.27Chapter 3.1.4 --- A Toy Example of POMDP: The NaiveBusInfo System --- p.27Chapter 3.2 --- The SDS-POMDP Model --- p.31Chapter 3.3 --- Composite Summary Point-based Value Iteration (CSPBVI) --- p.33Chapter 3.4 --- Application of SDS-POMDP Model: The Buslnfo System --- p.35Chapter 3.4.1 --- System Description --- p.35Chapter 3.4.2 --- Demonstration Description --- p.39Chapter 3.5 --- Chapter Summary --- p.42Chapter 4 --- Collecting User Judgments on Spoken Dialogs with Crowdsourcing --- p.46Chapter 4.1 --- Dialog Corpus and Automatic Dialog Classification --- p.47Chapter 4.2 --- User Judgments Collection with Crowdsourcing --- p.50Chapter 4.2.1 --- HITs on Dialog Evaluation --- p.51Chapter 4.2.2 --- HITs on Inter-rater Agreement --- p.53Chapter 4.2.3 --- Approval of Ratings --- p.54Chapter 4.3 --- Collected Results and Analysis --- p.55Chapter 4.3.1 --- Approval Rates and Comments from Mturk Workers --- p.55Chapter 4.3.2 --- Consistency between Automatic Dialog Classification and Manual Ratings --- p.57Chapter 4.3.3 --- Inter-rater Agreement Among Workers --- p.60Chapter 4.4 --- Comparing Experts to Non-experts --- p.64Chapter 4.4.1 --- Inter-rater Agreement on the Let's Go! System --- p.65Chapter 4.4.2 --- Consistency Between Expert and Non-expert Annotations on SDC Systems --- p.66Chapter 4.5 --- Chapter Summary --- p.68Chapter 5 --- Collaborative Filtering for Performance Prediction --- p.70Chapter 5.1 --- Item-Based Collaborative Filtering --- p.71Chapter 5.2 --- CF Model for User Satisfaction Prediction --- p.72Chapter 5.2.1 --- ICFM for User Satisfaction Prediction --- p.72Chapter 5.2.2 --- Extended ICFM for User Satisfaction Prediction --- p.73Chapter 5.3 --- Extraction of Interaction Features --- p.74Chapter 5.4 --- Experimental Results and Analysis --- p.76Chapter 5.4.1 --- Prediction of User Satisfaction --- p.76Chapter 5.4.2 --- Analysis of Prediction Results --- p.79Chapter 5.5 --- Verifying the Generalibility of CF Model --- p.81Chapter 5.6 --- Evaluation of The Buslnfo System --- p.86Chapter 5.7 --- Chapter Summary --- p.87Chapter 6 --- Conclusions and Future Work --- p.89Chapter 6.1 --- Thesis Summary --- p.89Chapter 6.2 --- Future Work --- p.90Bibliography --- p.9

Crowd-supervised training of spoken language systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-166).Spoken language systems are often deployed with static speech recognizers. Only rarely are parameters in the underlying language, lexical, or acoustic models updated on-the-fly. In the few instances where parameters are learned in an online fashion, developers traditionally resort to unsupervised training techniques, which are known to be inferior to their supervised counterparts. These realities make the development of spoken language interfaces a difficult and somewhat ad-hoc engineering task, since models for each new domain must be built from scratch or adapted from a previous domain. This thesis explores an alternative approach that makes use of human computation to provide crowd-supervised training for spoken language systems. We explore human-in-the-loop algorithms that leverage the collective intelligence of crowds of non-expert individuals to provide valuable training data at a very low cost for actively deployed spoken language systems. We also show that in some domains the crowd can be incentivized to provide training data for free, as a byproduct of interacting with the system itself. Through the automation of crowdsourcing tasks, we construct and demonstrate organic spoken language systems that grow and improve without the aid of an expert. Techniques that rely on collecting data remotely from non-expert users, however, are subject to the problem of noise. This noise can sometimes be heard in audio collected from poor microphones or muddled acoustic environments. Alternatively, noise can take the form of corrupt data from a worker trying to game the system - for example, a paid worker tasked with transcribing audio may leave transcripts blank in hopes of receiving a speedy payment. We develop strategies to mitigate the effects of noise in crowd-collected data and analyze their efficacy. This research spans a number of different application domains of widely-deployed spoken language interfaces, but maintains the common thread of improving the speech recognizer's underlying models with crowd-supervised training algorithms. We experiment with three central components of a speech recognizer: the language model, the lexicon, and the acoustic model. For each component, we demonstrate the utility of a crowd-supervised training framework. For the language model and lexicon, we explicitly show that this framework can be used hands-free, in two organic spoken language systems.by Ian C. McGraw.Ph.D

A parameterized and annotated spoken dialog corpus of the CMU Let's Go Bus Information System

Standardized corpora are the foundation for spoken language research. In this work, we introduce an annotated and standardized corpus in the Spoken Dialog Systems (SDS) domain. Data from the Let's Go Bus Information System from the Carnegie Mellon University in Pittsburgh has been formatted, parameterized and annotated with quality, emotion, and task success labels containing 347 dialogs with 9,083 system-user exchanges. A total of 46 parameters have been derived automatically and semi-automatically from Automatic Speech Recognition (ASR), Spoken Language Understanding (SLU) and Dialog Manager (DM) properties. To each spoken user utterance an emotion label from the set garbage, non-angry, slightly angry, very angry has been assigned. In addition, a manual annotation of Interaction Quality (IQ) on the exchange level has been performed with three raters achieving avalue of 0.54. The IQ score expresses the quality of the interaction up to each system-user exchange on a score from 1-5. The presented corpus is intended as a standardized basis for classification and evaluation tasks regarding task success prediction, dialog quality estimation or emotion recognition to foster comparability between different approaches on these fields

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia