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Speech and Language Interfaces, Applications, and Technologies
Published in Julie A. Jacko, The Human–Computer Interaction Handbook, 2012
Clare-Marie Karat, Jennifer Lai, Osamuyimen Stewart, Nicole Yankelovich
Most telephony systems are speaker-independent (i.e., no personalized training of the voice models required) speech recognition systems. They are also usually server based and must handle the signal degradation that occurs across the telephone lines. Telephony systems can be created from a combination of speech recognition and natural language processing (NLP) technologies. In telephony systems, a dialog manager component works with the speech recognition software to handle the course of the conversation with the user. The system provides feedback to the user through the dialog manager using recordings of either human voice or TTS. Telephony systems have capabilities such as “barge in” and “talk ahead” that enable the user to redirect the action of the system and complete multiple requests before being prompted for additional information necessary to complete the task. Conversational telephony systems include IVR systems and new systems built using voice XML (please see Section 16.3.5 on NLP for description of the technology). These systems work well and allow users to efficiently complete desired tasks.
EPMS for Customer Conversations
Published in Vivek Kale, Enterprise Process Management Systems, 2018
Spoken dialog systems are computer programs that receive speech as input and generate information as output synthesized speech, engaging the user in a dialog that aims to be similar to that between humans: Automatic speech recognition (ASR): The goal of speech recognition is to obtain the sequence of words uttered by a speaker. Once the speech recognizer has provided an output, the system must understand what the user said.Spoken language understanding module: The goal of spoken language understanding is to obtain the semantics from the recognized sentence. This process generally requires morphological, lexical, syntactical, semantic, discourse, and pragmatic knowledge.Dialog manager module: The dialog manager decides the next action of the system, interpreting the incoming semantic representation of the user input in the context of the dialog. In addition, it resolves ellipsis and anaphora, evaluates the relevance and completeness of user requests, identifies and recovers from recognition and understanding errors, retrieves information from data repositories, and decides about the system’s response.Natural language generation (NLG) module: Natural language generation is the process of obtaining sentences in natural language from the nonlinguistic, internal representation of information handled by the dialog system.Text-to-speech module: Text-to-speech transforms the generated sentences into synthesized speech.
Interactive Computing
Published in Vivek Kale, Digital Transformation of Enterprise Architecture, 2019
Spoken dialog systems are computer programs that receive speech as input and generate as output synthesized speech, engaging the user in a dialog that aims to be similar to that between humans: Automatic Speech Recognizer (ASR): The goal of speech recognition is to obtain the sequence of words uttered by a speaker. Once the speech recognizer has provided an output, the system must understand what the user said.Spoken Language Understanding (SLU) module: The goal of spoken language understanding is to obtain the semantics from the recognized sentence. This process generally requires morphological, lexical, syntactical, semantic, discourse and pragmatic knowledge.Dialog Manager (DM) module: The dialog manager decides the next action of the system, interpreting the incoming semantic representation of the user input in the context of the dialog. In addition, it resolves ellipsis and anaphora, evaluates the relevance and completeness of user requests, identifies and recovers from recognition and understanding errors, retrieves information from data repositories, and decides about the system’s response.Natural Language Generation (NLG) module: Natural language generation is the process of obtaining sentences in natural language from the non-linguistic, internal representation of information handled by the dialog system.Text-to-Speech (TTS) module: Text-to-speech transforms the generated sentences into synthesized speech.
Reasoning and communicative strategies in a model of argument-based negotiation
Published in Journal of Information and Telecommunication, 2018
The functions of the dialog manager can be formalized in terms of information state update which is changing during the interaction. There are different categories of update rules which will be used by a participant for moving from the current information state to the next one (Traum & Larsson, 2003). For example, there are the following rules for the computer when it plays B’s role.