An Overview of Basics Speech Recognition and Autonomous Approach for Smart Home IOT Low Power Devices

Fourniols, Jean-Yves; Nasreddine, Nadim; Escriba, Christophe; Acco, Pascal; Roux, Julien; Romero, Georges

doi:10.4236/jsip.2018.94015

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…A less CPU-intensive alternative to full speech recognition is keyword detection, where only a pre-defined vocabulary of spoken words is recognized. Such systems can even run on devices with much lower computational power than smartphones, such as 16-bit microcontrollers [25]. It has been argued that it would still be too taxing for mobile devices to listen out for the "millions or perhaps billions" of targetable keywords that could potentially be dropped in private conversations [51].…”

Section: Technical and Economic Feasibilitymentioning

confidence: 99%

Is My Phone Listening in? On the Feasibility and Detectability of Mobile Eavesdropping

Kröger

Raschke

2019

Data and Applications Security and Privacy XXXIII

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Besides various other privacy concerns with mobile devices, many people suspect their smartphones to be secretly eavesdropping on them. In particular, a large number of reports has emerged in recent years claiming that private conversations conducted in the presence of smartphones seemingly resulted in targeted online advertisements. These rumors have not only attracted media attention, but also the attention of regulatory authorities. With regard to explaining the phenomenon, opinions are divided both in public debate and in research. While one side dismisses the eavesdropping suspicions as unrealistic or even paranoid, many others are fully convinced of the allegations or at least consider them plausible. To help structure the ongoing controversy and dispel misconceptions that may have arisen, this paper provides a holistic overview of the issue, reviewing and analyzing existing arguments and explanatory approaches from both sides. Based on previous research and our own analysis, we challenge the widespread assumption that the spying fears have already been disproved. While confirming a lack of empirical evidence, we cannot rule out the possibility of sophisticated large-scale eavesdropping attacks being successful and remaining undetected. Taking into account existing access control mechanisms, detection methods, and other technical aspects, we point out remaining vulnerabilities and research gaps.

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Section: Technical and Economic Feasibilitymentioning

confidence: 99%

Is My Phone Listening in? On the Feasibility and Detectability of Mobile Eavesdropping

Kröger

Raschke

2019

Data and Applications Security and Privacy XXXIII

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“…Hence, many applications have been created in which speech to text technology plays an essential role [2][3]. These applications provide services, such as voice search, speech translation, personal assistant, and gaming [4][5]. The ASR systems comprise of four conceptually distinct stages: signal processing, feature extraction, acoustic model, and N-gram language model [6][7].…”

Section: Introductionmentioning

confidence: 99%

“…However, the environment of the audio signals is the main cause of noise and contrast in the speech signal [16]. The noise types may result from hundreds of sources, such as microphone quality, speaker characteristics, background sounds, and dialect differences [4]. Furthermore, various types of noise give different levels of errors, making it difficult to implement a filter technique for each type of noise or training the ASR on them [14].…”

Section: Introductionmentioning

confidence: 99%

An ensemble technique for speech recognition in noisy environments

Habeeb

Fadhil

Jurn

et al. 2020

IJEECS

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<span>Automatic speech recognition (ASR) is a technology that allows a computer and mobile device to recognize and translate spoken language into text. ASR systems often produce poor accuracy for the noisy speech signal. Therefore, this research proposed an ensemble technique that does not rely on a single filter for perfect noise reduction but incorporates information from multiple noise reduction filters to improve the final ASR accuracy. The main factor of this technique is the generation of K-copies of the speech signal using three noise reduction filters. The speech features of these copies differ slightly in order to extract different texts from them when processed by the ASR system. Thus, the best among these texts can be elected as final ASR output. The ensemble technique was compared with three related current noise reduction techniques in terms of CER and WER. The test results were encouraging and showed a relatively decreased by 16.61% and 11.54% on CER and WER compared with the best current technique. ASR field will benefit from the contribution of this research to increase the recognition accuracy of a human speech in the presence of background noise.</span>

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