Efficient client–server based implementations of mobile speech recognition services

Rose, Richard C.; Arizmendi, Iker

doi:10.1016/j.specom.2006.07.004

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…There are, however, limitations in the application of such technology. These limitations include, but are not limited to, the operational requirement for such systems to maintain a constant client-server connection and the need for efficient deployment of normalisation techniques to accommodate different speaker accents [1]. In addition, where the speech technology is used for security purposes, the need for the voice utterances to be transmitted over a network introduces security concerns.…”

Section: Introductionmentioning

confidence: 99%

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

Mustafa

Allen

Appiah

2017

Neural Comput & Applic

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The adoption of high-accuracy speech recognition algorithms without an effective evaluation of their impact on the target computational resource is impractical for mobile and embedded systems. In this paper, techniques are adopted to minimise the required computational resource for an effective mobile-based speech recognition system. A Dynamic MultiLayer Perceptron speech recognition technique, capable of running in real time on a state-of-the-art mobile device, has been introduced. Even though a conventional hidden Markov model when applied to the same dataset slightly outperformed our approach, its processing time is much higher. The Dynamic Multi-layer Perceptron presented here has an accuracy level of 96.94% and runs significantly faster than similar techniques.

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Section: Introductionmentioning

confidence: 99%

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

Mustafa

Allen

Appiah

2017

Neural Comput & Applic

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“…This example is derived based on a speech recognition application [7]. There are two source nodes, s0 and s1, each streaming a 1s utterance of 8-bit, 4-kHz speech request (referred as a task with 4000 bytes data size) to one of the four processing nodes, n0, n1, n2, and n3.…”

Section: An Illustrative Examplementioning

confidence: 99%

“…However, when the overhead messages occupy a large amount of network bandwidth (at TC interval of 0.2s), schedulers are unable to predict the accurate transmission time at the target processing node as in Eqn. (7). The scheduling results are significantly impacted, especially for greedy and probabilistic schedulers, which result in a dramatic drop of the task completion rates.…”

Section: Effect Of Varying Topology Control Message Intervalmentioning

confidence: 99%

“…The offloading decisions are usually constructed by analyzing parameters, including bandwidths, computation speeds, power coefficients, and task queues of all participating devices. One of the benefits of offloading is to meet a stringent response time requirement in mobile systems, particularly for real-time applications such as object recognition [3], disaster forecast [4], real-time video coding [5], and speech recognition [7]. These applications typically involve large computation using small datasets.…”

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confidence: 99%

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An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

Shi

Yang

et al. 2016

Pervasive and Mobile Computing

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An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds, Pervasive and Mobile Computing (2015), http://dx. ABSTRACTMobile Cloud Computing (MCC) enables mobile devices to use resource providers other than mobile devices themselves to host the execution of mobile applications. Various mobile cloud architectures and scheduling algorithms have been studied recently. However, how to utilize MCC to enable mobile devices to run complex real-time applications while keeping high energy efficiency remains a challenge. In this paper, firstly, we introduce the local mobile clouds formed by nearby mobile devices and give the mathematical models of the mobile devices and their applications. Secondly, we formulate the scheduling problem in local mobile clouds. After describing the resource discovery scheme and the adaptive, probabilistic scheduling algorithm, we finally validate the performance of the proposed algorithm by simulation experiments.

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“…In fact, servers provide the necessary security of a system that customers work on, for instance in some organizations some systems provide some level of safety for using the database [8].…”

Section: Computer Servermentioning

confidence: 99%

The Evaluation of Timing Performance for Different Smartphone Devices via Single Server

2016

IJCCIE

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Abstract-These instructions give you guidelines for preparing papers for conferences or journals. Use this document as a template if you are using Microsoft Word. Otherwise, use this document as an instruction set. The electronic file of your paper will be formatted further at ICEHM. Define all symbols used in the abstract. Do not cite references in the abstract. Do not delete the blank line immediately above the abstract; it sets the footnote at the bottom of this column. Page margins are 1,78 cm top and down; 1,65 cm left and right. Each column width is 8,89 cm and the separation between the columns is 0,51 cm.

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Efficient client–server based implementations of mobile speech recognition services

Cited by 6 publications

References 13 publications

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

The Evaluation of Timing Performance for Different Smartphone Devices via Single Server

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