Mazin Gilbert

Intelligent virtual agents for contact center automation

The explosion of multimedia data, the continuous growth in computing power, and advances in machine learning and speech and natural language processing are making it possible to create a new breed of virtual intelligent agents capable of performing sophisticated and complex tasks that are radically transforming contact centers. These virtual agents are enabling ubiquitous and personalized access to communication services from anywhere. They ultimately provide a vehicle to fully automate eContact services without agent personnel. They are not limited to multimodal, multimedia, and multilingual capabilities, but also possess learning and data-mining capabilities to enable them to scale and self-maintain as well as extract and report on business intelligence. AT&T VoiceTone is a subset of this eContact revolution focused on creating this new wave of intelligent communication services.

EMOTION DETECTION IN EMAIL CUSTOMER CARE

Prompt and knowledgeable responses to customers’ email are critical in maximizing customer satisfaction. Such messages often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these email messages as emotional email. They provide valuable feedback to improve contact center efficiency and the quality of the overall customer care experience, which in turn results in increased customer retention. We describe a method that uses salient features to identify emotional email in the customer care domain. Salient features in customer care related email are expressions of customer frustration, dissatisfaction with the business, and threats to either leave, take legal action, and/or report to authorities. Compared to a baseline system using word unigram features, our proposed approach significantly improves emotional email detection performance.

A learning approach to discovering Web page semantic structures

This paper proposes a learning approach for discovering the semantic structure of Web pages. The task includes partitioning the text on a Web page into information blocks and identifying their semantic categories. We employed two machine learning techniques, Adaboost and SVMs, to learn from a labeled Web page corpus. We evaluated our approach on general Web pages from the World Wide Web and obtained encouraging results. This work can be beneficial to a number of Web-driven applications such as search engines, Web-based question answering, Web-based data mining as well as voice enabled Web navigation.

LET'S DISCOH: COLLECTING AN ANNOTATED OPEN CORPUSWITH DIALOGUE ACTS AND REWARD SIGNALS FOR NATURAL LANGUAGE HELPDESKS

We motivate and explain the DlSCoH project, which uses a publicly deployed spoken dialogue system for conference services to collect a richly annotated corpus of mixed-initiative human- machine spoken dialogues. System users are able to call a phone number and learn about a conference, including paper submission, program, venue, accommodation options and costs, etc. The collected corpus is (1) usable for training, evaluating and comparing statistical models, (2) naturally spoken and task oriented, (3) extendible / generalizable, (4) collected using state-of-the-art research and commercial technology, (5) freely available to researchers. We explain the principles behind the dialogue context representations and reward signals collected by the system, as well as the overall system design, call types, and call flow. We also present results regarding the initial ASR models and spoken language understanding models. We expect the resulting corpora to be used in advanced dialogue research over the coming years.

Webtalk: Towards Automatically Building Spoken Dialog Systems Through Miningwebsites

WebTalk is a system for analyzing unstructured information from company Websites to support automatic creation of spoken dialog applications. The goal is to completely automate the process of building, maintaining and deploying dialog applications by leveraging the wealth of information on the World Wide Web. WebTalk employs technologies in Web mining, document understanding, question/answering, and speech and language processing. In this paper, we review extensions to these technologies to make them suitable for creating a WebTalk application. We present an evaluation study of a WebTalk spoken dialog system that has been instantiated on a telecom company Website. Experiments with 30 different scenarios indicate promising results and provide evidence that such systems can potentially revolutionize the paradigm for creating and scaling spoken dialog services

Bootstrapping spoken dialogue systems by exploiting reusable libraries

Building natural language spoken dialogue systems requires large amounts of human transcribed and labeled speech utterances to reach useful operational service performances. Furthermore, the design of such complex systems consists of several manual steps. The User Experience (UE) expert analyzes and defines by hand the system core functionalities: the system semantic scope (call-types) and the dialogue manager strategy that will drive the human–machine interaction. This approach is extensive and error-prone since it involves several nontrivial design decisions that can be evaluated only after the actual system deployment. Moreover, scalability is compromised by time, costs, and the high level of UE know-how needed to reach a consistent design. We propose a novel approach for bootstrapping spoken dialogue systems based on the reuse of existing transcribed and labeled data, common reusable dialogue templates, generic language and understanding models, and a consistent design process. We demonstrate that our approach reduces design and development time while providing an effective system without any application-specific data.

Introduction to the Special Issue on Data Mining of Speech, Audio, and Dialog

ATA mining is concerned with the science, technology, and engineering of discovering patterns and extracting potentially useful or interesting information automatically or semi-automatically from data. Data mining was introduced in the 1990s and has deep roots in the fields of statistics, artificial intelligence, and machine learning. With the advent of inexpensive storage space and faster processing over the past decade or so, data mining research has started to penetrate new grounds in areas of speech and audio processing as well as spoken language dialog. It has been fueled by the influx of audio data that are becoming more widely available from a variety of multimedia sources including webcasts, conversations, music, meetings, voice messages, lectures, television, and radio. Algorithmic advances in automatic speech recognition have also been a major, enabling technology behind the growth in data mining. Current state-of-the-art, large-vocabulary, continuous speech recognizers are now trained on a record amount of data—several hundreds of millions of words and thousands of hours of speech. Pioneering research in robust speech processing, large-scale discriminative training, finite state automata, and statistical hidden Markov modeling have resulted in real-time recognizers that are able to transcribe spontaneous speech with a word accuracy exceeding 85%. With this level of accuracy, the technology is now highly attractive for a variety of speech mining applications. Speech mining research includes many ways of applying machine learning, speech processing, and language processing algorithms to benefit and serve commercial applications. It also raises and addresses several new and interesting fundamental research challenges in the areas of prediction, search, explanation, learning, and language understanding. These basic challenges are becoming increasingly important in revolutionizing business processes by providing essential sales and marketing information about services, customers, and product offerings. They are also enabling a new class of learning systems to be created that can infer knowledge and trends automatically from data, analyze and report application performance, and adapt and improve over time with minimal or zero human involvement. Effective techniques for mining speech, audio, and dialog data can impact numerous business and government applications. The technology for monitoring conversational speech to discover patterns, capture useful trends, and generate alarms is essential for intelligence and law enforcement organizations as well as for enhancing call center operation. It is useful for an

Control loop automation management platform (CLAMP)

AT&T is embarking on an exciting journey to revolutionize its network by transforming itself into a software company running the largest and most intelligent programmable cloud on the planet. Indeed, the network of Domain 2.0 (D2) will be intelligent software systems and applications operating on general-purpose commodity hardware [1]. This transformation will not only drive down CAPEX, OPEX and help to configure our network with less human intervention, but it will also create significant opportunities to scale and monetize existing and new intelligent services. This transformation will enable AT&Ts D2 to establish a new services ecosystem equivalent in concept to the application ecosystem adopted by the Apple iOS and Android. D2 will facilitate mass marketing existing and new services, and lower the barrier to entry for enterprise and small business customers to create new innovative services.

Autonomous services composition in domain 2

AT&T is embarking on an exciting journey to revolutionize its network by transforming itself into a software company running the largest and most intelligent programmable cloud on the planet. Indeed, the network of Domain 2.0 (D2) will be intelligent software systems and applications operating on general-purpose commodity hardware [1]. This transformation will not only drive down CAPEX, OPEX and help to configure our network with less human intervention, but it will also create significant opportunities to scale and monetize existing and new intelligent services. This transformation will enable AT&Ts D2 to establish a new services ecosystem equivalent in concept to the application ecosystem adopted by the Apple iOS and Android. D2 will facilitate mass marketing existing and new services, and lower the barrier to entry for enterprise and small business customers to create new innovative services.

The Business of Speech Technologies

With the fast pace of developments of communications networks and devices, immediate and easy access to information and services is now the expected norm. Several critical technologies have entered the marketplace as key enablers to help make this a reality. In particular, speech technologies, such as speech recognition and natural language understanding, have changed the landscape of how services are provided by businesses to consumers forever. In 30 short years, speech has progressed from an idea in research laboratories across the world, to a multibillion-dollar industry of software, hardware, service hosting, and professional services. Speech is now almost ubiquitous in cell phones. Yet, the industry is still very much in its infancy with its focus being on simple low hanging fruit applications of the technologies where the current state of technology actually fits a specific market need, such as voice enabling of call center services or voice dialing over a cell phone.