By João Freitas, António Teixeira, Miguel Sales Dias, Samuel Silva
This e-book offers a extensive and finished review of the present technical ways within the sector of silent speech interfaces (SSI), either in thought and in program. every one method is defined within the context of the human speech creation strategy, permitting the reader to obviously comprehend the rules in the back of SSI ordinarily and throughout diverse equipment. also, the e-book explores the mixed use of alternative information assets, accrued from a variety of sensors, to be able to take on the restrictions of easier SSI techniques, addressing present demanding situations of this box. The publication additionally offers information regarding current SSI purposes, assets and an easy instructional on tips to construct an SSI.
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Additional resources for An Introduction to Silent Speech Interfaces
2007; McLoughlin 2014), gesture recognition (Kalgaonkar and Raj 2009), speaker recognition (Kalgaonkar and Raj 2008), speech synthesis (Toth et al. 2010), and speech recognition (Freitas et al. 2012; Srinivasan et al. 2010). Regarding speech recognition, ultrasonic devices were first applied to ASR in 1995 using an ultrasonic lip motion detector by Jennings and Ruck (1995). In this work, the “ultrasonic mike,” as the authors call it, is used as an input to an automatic lip-reader with the aim of improving ASR in noisy environments, by combining it with a conventional ASR system.
Using ECoG, the authors performed a classiﬁcation of phonemes for American English with up to 36 % accuracy, when considering all phonemes, and up to 63 % for a single phoneme. Herff et al. (2015) presented Brain-to-Text, a system that transforms brain activity resulting from overt speech production into the corresponding textual representation, presenting word error rates as low as 25 % and phone error rates below 50 %. Chakrabarti et al. (2015), in the context of a review on the use of ECoG for speech decoding, discuss the challenges that still need to be addressed to reach a practical speech neuroprosthetic.
2009). The third method is a hybrid version of the first and second methods and combines features from both methodologies, either as a joint shape appearance vector, or as a cooperative statistical model learned from both sets of features. Appearance-based methods, due to their simplicity and efficiency, are the most popular (Yaling et al. 2010). The challenge in extracting features from video resides in collecting required information from the vast amounts of data present in image sequences. Each RGB frame contains a large amount of pixels that is usually too large to model as a feature vector.
An Introduction to Silent Speech Interfaces by João Freitas, António Teixeira, Miguel Sales Dias, Samuel Silva