Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

The use of hidden Markov models for speech recognition has become predominant in the last several years, as evidenced by the number of published papers and talks at major speech conferences. The reasons this method has become so popular are the inherent statistical (mathematically precise) framework; the ease and availability of training algorithms for cstimating the parameters of the models from finite training sets of speech data; the flexibility of the resulting recognition system in which one can easily change the size, type, or architecture of the models to suit particular words, sounds, and so forth; and the ease of implementation of the overall recognition system. In this expository article, we address the role of statistical methods in this powerful technology as applied to speech recognition and discuss a range of theoretical and practical issues that are as yet unsolved in terms of their importance and their effect on performance for different system implementations.

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Hidden Markov models for speech recognition

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A mobile terminal including a body; a touchscreen provided to a front and extending to side of the body and configured to display content; and a controller configured to detect one side of the body comes into contact with one side of an external terminal, display a first area on the touchscreen corresponding to a contact area of the body and the external terminal and a second area including the content, receive an input of moving the content displayed in the second area to the first area, display the content in the first area, and share the content in the first area with the external terminal.

Mobile terminal and method for controlling the same

The present invention relates to: speech recognition using selectable recognition modes; using choice lists in large-vocabulary speech recognition; enabling users to select word transformations; speech recognition that automatically turns recognition off in one or more specified ways; phone key control of large-vocabulary speech recognition; speech recognition using phone key alphabetic filtering and spelling: speech recognition that enables a user to perform re-utterance recognition; the combination of speech recognition and text-to-speech (TTS) generation; the combination of speech recognition with handwriting and/or character recognition; and the combination of large-vocabulary speech recognition with audio recording and playback.

Methods, systems, and programming for performing speech recognition

In this paper, we introduce the new paradigm used in the most recent ARPA-sponsored Continuous Speech Recognition (CSR) evaluation and then discuss the important features of the test design.The 1993 CSR evaluation was organized in a novel fashion in an attempt to accomodate research over a broad variety of important problems in CSR while maintaining a clear program-wide research focus. Furthermore, each test component in the evaluation was designed as an experiment to extract as much information as possible from the results.The evaluation was centered around a large vocabulary speaker-independent (SI) baseline test, which was required of every participating site. This test was dubbed the 'Hub' since it was common to all sites and formed the basis for controlled inter-system comparisons.The Hub test was augmented with a variety of problem-specific optional tests designed to explore a variety of important problems in CSR, mostly involving some kind of mismatch between the training and test conditions. These tests were known as the 'Spokes' since they all could be informatively compared to the Hub, but were otherwise independent.In the first trial of this evaluation paradigm in November, 1993, 11 research groups participated, yielding a rich array of comparative and contrastive results, all calibrated to the current state of the art in large vocabulary CSR.

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The hub and spoke paradigm for CSR evaluation

One aspect of the invention involves word recognition that uses scrollable choice lists in which choices are listed in character-order. Another aspect relates to a scrollable, visually-displayed word recognition choice list, where the recognition candidates on the choice list are each associated with a choice-selecting symbol the user can use to select a desired recognition candidate by pressing an associated button, and where the same choice-selecting symbol is used for different choices displayed on the display at different times as a result of scrolling. Another aspect of the invention relates to providing a choice list of best scoring characters for a particular character position in the spelling of a filter that is used to filter word recognition. Another aspect of the invention relates to a choice list used in word recognition in which the choice list can be scrolled horizontally.

Word recognition using choice lists

The present invention relates to speech recognition using selectable recognition modes. This includes innovations such as: large vocabulary speech recognition programming that supplies recognized words to external program as they are recognized, and allows a user to select between large vocabulary recognition of an utterance with and without language context from the prior utterance independently of state of the external program; allowing a user to select between continuous and discrete speech recognition that use substantially the same vocabulary; allowing a user to select between continuous and discrete large-vocabulary speech recognition modes; allowing a user to select between at least two different alphabetic entry speech recognition modes; and allowing a user to select from among four or more of the following recognitions modes when creating text: a large-vocabulary mode, an alphabetic entry mode, a number entry mode, and a punctuation entry mode.

Speech recognition using selectable recognition modes

Text-to-speech (TTS) generation is used in conjunction with large vocabulary speech recognition to say words selected by the speech recognition. The software for performing the large vocabulary speech recognition can share speech modeling data with the TTS software. TTS or recorded audio can be used to automatically say both recognized text and the names of recognized commands after their recognition. The TTS can automatically repeats text recognized by the speech recognition after each of a succession of end of utterance detections. A user can move a cursor back or forward in recognized text, and the TTS can speak one or more words at the cursor location after each such move. The speech recognition can be used to produces a choice list of possible recognition candidates and the TTS can be used to provide spoken output of one or more of the candidates on the choice list.

Jordan Cohen

Papers

Methods, systems, and programming for performing speech recognition

The hub and spoke paradigm for CSR evaluation

Word recognition using choice lists

Speech recognition using selectable recognition modes

Combined speech recongnition and text-to-speech generation