A robust high accuracy speech recognition system for mobile applications

Deligne, Sabine; Dharanipragada, S.; Gopinath, R.; Maison, B.; Olsen, Peder A.; Printz, Harry

doi:10.1109/tsa.2002.804541

Cited by 28 publications

(12 citation statements)

References 13 publications

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“…In addition to their use in voice calls, researchers have recently tried to develop different applications based upon the sensing capabilities of a mobile phone's microphone. A very successful example is that of speech recognition systems [Deligne et al, 2002], which are widely implemented in current mobile phones. These systems enable users to operate the mobile phone by means of voice command without a keyboard.…”

Section: Microphonementioning

confidence: 99%

A survey on smartphone-based systems for opportunistic user context recognition

2013

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The ever growing computation and storage capability of mobile phones has given rise to mobile centric context recognition systems, which are able to sense and analyze the context of the carrier so as to provide an appropriate level of service. Particularly, as nonintrusive autonomous sensing and context recognition are one of the most desirable characteristics of a personal sensing system; commendable efforts have been made to develop opportunistic sensing techniques on mobile phones. The resulting combination of these approaches has ushered in a new realm of applications, namely opportunistic user context recognition with mobile phones. This article surveys the existing research and approaches towards realization of such systems. In doing so, the typical architecture of a mobile centric user context recognition system as a sequential process of sensing, pre-processing and context recognition phases are introduced. The main techniques used for the realization of the respective processes during these phases are described, highlighting strengths and limitations of those. In addition, lessons learned from previous approaches are presented as motivation for future research. Finally, several open challenges are discussed as possible ways to extend the capabilities of current systems and improve their real-world experience.

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Section: Microphonementioning

confidence: 99%

A survey on smartphone-based systems for opportunistic user context recognition

2013

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“…However, studies (Zhang et al, 2000) have shown that low-bandwidth connections to the server can result in significant degradation of speech recognition quality. In contrast, local speech recognition (Deligne et al;Varga et al, 2002) on the mobile device eliminates the need for high-speed communication. Local speech recognition limits the kinds of mobile devices that are powerful enough to perform speech processing; however, the computing power of mobile devices is increasing.…”

Section: Related Workmentioning

confidence: 99%

Managing multiple speech‐enabled applications in a mobile handheld device

Schuricht

Davis

et al. 2009

International Journal of Pervasive Computing and Communications

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -Mobile handheld devices, such as cellular phones and personal digital assistants, are inherently small and lack an intuitive and natural user interface. Speech recognition and synthesis technology can be used in mobile handheld devices to improve the user experience. The purpose of this paper is to describe a prototype system that supports multiple speech-enabled applications in a mobile handheld device. Design/methodology/approach -The main component of the system, the Program Manager, coordinates and controls the speech-enabled applications. Human speech requests to, and responses from, these applications are processed in the mobile handheld device, to achieve the goal of humanlike interactions between the human and the device. In addition to speech, the system also supports graphics and text, i.e., multimodal input and output, for greater usability, flexibility, adaptivity, accuracy, and robustness. The paper presents a qualitative and quantitative evaluation of the prototype system. The Program Manager is currently designed to handle the specific speech-enabled applications that we developed. Findings -The paper determines that many human interactions involve not single applications but multiple applications working together in possibly unanticipated ways. Research limitations/implications -Future work includes generalization of the Program Manager so that it supports arbitrary applications and the addition of new applications dynamically. Future work also includes deployment of the Program Manager and the applications on cellular phones running the Android Platform or the Openmoko Framework. Originality/value -This paper presents a first step towards a future human interface for mobile handheld devices and for speech-enabled applications operating on those devices.

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“…Lowering the number of Gaussians to reduce the CPU cost is effective up to a certain degree. If the observation model is less accurate, the search the number of Gaussian components to each state (Deligne et al 2002). The objecThe theoretical upper limit on the number of Gaussian components is given by during the model building only if they significantly contribute to the likelihood increase.…”

Section: Model Organizationmentioning

confidence: 99%

Algorithm Optimizations: Low Computational Complexity

Novák¹

2008

Advances in Pattern Recognition

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Abstract. Advances in ASR are driven by both scientific achievements in the field and the availability of more powerful hardware. While very powerful CPUs allow us to use ever more complex algorithms in server-based large vocabulary ASR systems (e.g. in telephony applications), the capability of embedded platforms will always lag behind. Nevertheless as the popularity of ASR application grows, we can expect an increasing demand for functionality on based applications by natural language understanding (NLU) systems leads to increased vocabulary sizes and thus the need for greater CPU performance. In this chapter we present an overview of ASR decoder design options with an emphasis on techniques which are suitable for embedded platforms. One needs to keep in mind that there is no one-size-fits-all solution; specific algorithmic improvements may only be best applied to highly restricted applications or scenarios. The optimal solution can usually be achieved by making choices with respect to algorithms aimed at maximizing specific benefits for a particular platform and task. IntroductionWhile systems for dealing with large vocabulary recognition in real-time ASR have been available for many years, deployment on platforms with limited resources presents new challenges with respect to many aspects of the overall ASR system design. There is an obvious tendency to utilize any new beneficial technique over the wide spectrum of ASR applications and platforms. But in numerous respects embedded platforms lag behind the state of the art in speech recognition with respect to general CPU architectures. The limitations of the embedded platforms affecting this state of affairs includes: lower CPU clock speeds, limited or missing ability to process floating point operations, and restricted memory capacity both with respect to speed and size having implications to all levels of caching. Those constraints are bad enough, but added to this is the relatively restricted capability of tools/platforms tailored specifically to embedded systems development, which results in many important language features (e.g., templates in the C++ case) not being fully supported.Hence any new algorithm targeting embedded platforms must therefore be evaluated in light of the above restrictions. Possible actions that one needs to consider taking may include finding more efficient implementation of a given algorithm, finding and applying an even more efficient algorithm with the same or similar results, embedded platforms as well. For example, replacing simple command and control grammar-

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A robust high accuracy speech recognition system for mobile applications

Cited by 28 publications

References 13 publications

A survey on smartphone-based systems for opportunistic user context recognition

A survey on smartphone-based systems for opportunistic user context recognition

Managing multiple speech‐enabled applications in a mobile handheld device

Algorithm Optimizations: Low Computational Complexity

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