The SPHINX-II speech recognition system: an overview

Huang, Xuedong; Alleva, Fil; Hon, Hsiao‐Wuen; Hwang, Mei‐Yuh; Rosenfeld, Roni

doi:10.1006/csla.1993.1007

Cited by 252 publications

(95 citation statements)

References 33 publications

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“…As shown on the left of the figure, student speech is sent to the Sphinx2 speech recognizer [34] after being digitized from head-mounted microphone input. The speech recognizer used the default acoustic models distributed with the Sphinx2 software, along with 15 stochastic (trigram) language models which support a vocabulary of 681 words and were trained on the UNC-ITSPOKE-WOZ corpus and other prior ITSPOKE corpora.…”

Section: Itspoke: Non-adaptive System Behaviormentioning

confidence: 99%

Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor

Forbes-Riley

Litman

2011

Speech Communication

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Section: Itspoke: Non-adaptive System Behaviormentioning

confidence: 99%

Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor

Forbes-Riley

Litman

2011

Speech Communication

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“…For the speech-to-text converter, any suitable converter may be used; for example, we may use Sphinx speech recognition system [43][44][45].…”

Section: Personal Assistant Systemsmentioning

confidence: 99%

Automatic Task Classification via Support Vector Machine and Crowdsourcing

Shin

Paek

2018

Mobile Information Systems

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Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets. Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public. In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing. To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform. Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices. Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%. By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.

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“…Sphinx Speech Recognition is used for the translation of spoken words to text [5] by taking the raw waveform, splitting it on utterances by silences, and attempting to recognize the word in each utterance. We group all possible combinations of words and the best matching combination is chosen.…”

Section: Benchmark Descriptionsmentioning

confidence: 99%

CortexSuite: A synthetic brain benchmark suite

Thomas

Gohkale

Tanuwidjaja

et al. 2014

2014 IEEE International Symposium on Workload Characterization (IISWC)

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Abstract-These days, many traditional end-user applications are said to "run fast enough" on existing machines, so the search continues for novel applications that can leverage the new capabilities of our evolving hardware. Foremost of these potential applications are those that are clustered around information processing capabilities that humans have today but are lacking in computers. The fact that brains can perform these computations serves as an existence proof that these applications are realizable. At the same time, we often discover that the human nervous system, with its 80 billion neurons, on some metrics, is more powerful and energy-efficient than today's machines. Both of these aspects make this class of applications a desirable target for an architectural benchmark suite, because there is evidence that these applications are both useful and computationally challenging.This paper details CortexSuite, a Synthetic Brain Benchmark Suite, which seeks to capture this workload. We classify and identify benchmarks within CortexSuite by analogy to the human neural processing function. We use the major lobes of the cerebral cortex as a model for the organization and classification of data processing algorithms. To be clear, our goal is not to emulate the brain at the level of the neuron, but rather to collect together synthetic, man-made algorithms that have similar function and have met with success in the real world. We consulted six worldclass machine learning and computer vision researchers, who collectively hold 83,091 citations across their distinct subareas, asking them to identify newly emerging computationally-intensive algorithms or applications that are going to have a large impact over the next ten years. This is coupled with datasets that reflect the philosophy of practical use algorithms and are coded in "clean C" so as to make them accessible, analyzable, and usable for parallel and approximate compiler and architecture researchers alike.

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The SPHINX-II speech recognition system: an overview

Cited by 252 publications

References 33 publications

Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor

Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor

Automatic Task Classification via Support Vector Machine and Crowdsourcing

CortexSuite: A synthetic brain benchmark suite

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