Electrolarynx in voice rehabilitation

Liu, Hanjun; Ng, Manwa L.

doi:10.1016/j.anl.2006.11.010

Cited by 67 publications

(45 citation statements)

References 32 publications

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“…There are currently limited options to assist speech communication for those individuals (e.g., esophageal speech, tracheo-esophageal speech or tracheo-esophageal puncture (TEP) speech, and electrolarynx). These approaches, however, produce an abnormal sounding voice [17,18], which impacts the quality of life of laryngectomees. Current text-to-speech technologies have been able to produce speech with natural sounding voice for SSIs [19].…”

Section: Introductionmentioning

confidence: 99%

Determining an Optimal Set of Flesh Points on Tongue, Lips, and Jaw for Continuous Silent Speech Recognition

Wang¹,

Hahm²,

Mau³

2015

Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

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Articulatory data have gained increasing interest in speech recognition with or without acoustic data. Electromagnetic articulograph (EMA) is one of the affordable, currently used techniques for tracking the movement of flesh points on articulators (e.g., tongue) during speech. Determining an optimal set of sensors is important for optimizing the clinical applications of EMA data, due to the inconvenience of attaching sensors on tongue and other intraoral articulators, particularly for patients with neurological diseases. A recent study found an optimal set (tongue tip and body back, upper and lower lips) on tongue and lips for isolated phoneme, word, or short phrase classification from articulatory movement data. This four-sensor set, however, has not been verified in continuous silent speech recognition. In this paper, we investigated the use of data from sensor combinations in continuous speech recognition to verify the finding using a publicly available data set MOCHA-TIMIT. The long-standing speech recognition approach Gaussian mixture model (GMM)-hidden Markov model (HMM) and a recently available approach deep neural network (DNN)-HMM were used as the recognizers. Experimental results confirmed that the four-sensor set is optimal out of the full set of sensors on tongue, lips, and jaw. Adding upper incisor and/or velum data further improved the recognition performance slightly.

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Section: Introductionmentioning

confidence: 99%

Determining an Optimal Set of Flesh Points on Tongue, Lips, and Jaw for Continuous Silent Speech Recognition

Wang¹,

Hahm²,

Mau³

2015

Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

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“…However, there are currently only a limited number of post-laryngectomy voice restoration methods available for these individuals: esophageal speech, the electrolarynx and speech valves. Unfortunately, these methods are often limited by their usability and/or the abnormal voice produced, which may be hard to understand for listeners [1,[3][4]. On the other hand, typing-based augmented and alternative communication (AAC) devices are limited by slow manual text input [5].…”

Section: Introductionmentioning

confidence: 99%

Towards an Intraoral-Based Silent Speech Restoration System for Post-laryngectomy Voice Replacement

Cheah

Gilbert

González

et al. 2017

Biomedical Engineering Systems and Technologies

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Abstract. Silent Speech Interfaces (SSIs) are alternative assistive speech technologies that are capable of restoring speech communication for those individuals who have lost their voice due to laryngectomy or diseases affecting the vocal cords. However, many of these SSIs are still deemed as impractical due to a high degree of intrusiveness and discomfort, hence limiting their transition to outside of the laboratory environment. We aim to address the hardware challenges faced in developing a practical SSI for post-laryngectomy speech rehabilitation. A new Permanent Magnet Articulography (PMA) system is presented which fits within the palatal cavity of the user's mouth, giving unobtrusive appearance and high portability. The prototype is comprised of a miniaturized circuit constructed using commercial off-the-shelf (COTS) components and is implemented in the form of a dental retainer, which is mounted under roof of the user's mouth and firmly clasps onto the upper teeth. Preliminary evaluation via speech recognition experiments demonstrates that the intraoral prototype achieves reasonable word recognition accuracy and is comparable to the external PMA version. Moreover, the intraoral design is expected to improve on its stability and robustness, with a much improved appearance since it can be completely hidden inside the user's mouth. [8]. To date, a number of SSIs have been proposed in an attempt to extract non-acoustic information generated during speech production and reproduce audible speech using different sensing modalities, such as measuring electrical activities of the brain [9][10][11] or the articulator muscles [12][13][14], or by capturing movements of the speech articulators themselves [3,5,8,[15][16][17][18][19]. A comprehensive summary on different SSIs technologies were presented in [8]. Because of their unique feature, SSIs can also be deployed in acoustically challenging environment or where privacy/confidentially is desirable, and not limited to its use as a communication aid for speech impaired individuals. KeywordsDespite the attractive attributes of SSIs, there are still challenges in the form of hardware (e.g. portability, lightweight, unobtrusiveness and wearability) and processing software (e.g. efficiency, robustness and intelligibility speech generation). Preliminary discussions on the influential factors affecting the SSIs' implementation were presented in [8], based upon criteria such as ability to operate in silence and noisy environments, usability by laryngectomees, issue of invasiveness market reediness and cost.In the present work we employ the Permanent Magnet Articulography (PMA), which is a type SSI that is based on sensing the changes in the magnetic field generated by a set of permanent magnet markers attached onto the vocal apparatus (i.e. lips and tongue) during speech articulation by using an array of magnetic sensors located around the mouth [1,3]. Although PMA shares some similarities with Electromagnetic Articulography (EMA) [5,17], it does not explicitly provide the ...

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“…In 2013, about 12,260 new cases of laryngeal cancer were estimated in the United States (American Cancer Society, 2013). Currently, there are only limited treatment options for these individuals including (1) esophageal speech, which involves oscillation of the esophagus and is difficult to learn; (2) tracheo-esophageal speech, in which a voice prosthesis is placed in a tracheo-esophageal puncture; and (3) electrolarynx, an external device held on the neck during articulation, which produces a robotic voice quality (Liu and Ng, 2007). Perhaps the greatest disadvantage of these approaches is that they produce abnormal sounding speech with a fundamental frequency that is low and limited in range.…”

Section: Introductionmentioning

confidence: 99%

“…Perhaps the greatest disadvantage of these approaches is that they produce abnormal sounding speech with a fundamental frequency that is low and limited in range. The abnormal voice quality output severely affects the social life of people after laryngectomy (Liu and Ng, 2007). In addition, the tracheo-esophageal option requires an additional surgery, which is not suitable for every patient (Bailey et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 5th Workshop on Speech and Language Processing for Assistive Technologies

2014

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Augmentative Alternative Communication (AAC) policy suffers from a lack of large scale quantitative evidence on the demographics of users and diversity of devices.The 2013 Domesday Dataset was created to aid formation of AAC policy at the national level. The dataset records purchases of AAC technology by the UK's National Health Service between 2006 and 2012; giving information for each item on: make, model, price, year of purchase, and geographic area of purchase. The dataset was designed to help answer open questions about the provision of AAC services in the UK; and the level of detail of the dataset is such that it can be used at the research level to provide context for researchers and to help validate (or not) assumptions about everyday AAC use. This paper examine three different ways of using the Domesday Dataset to provide verified evidence to support, or refute, assumptions, uncover important research problems, and to properly map the technological distinctiveness of a user community. IntroductionTechnical researchers in the AAC community are required to make certain assumptions about the state of the community when choosing research projects that are calculated to make the most effective use of research resources for the greatest possible benefit.A particular issue is estimating how easily technical research can achieve wide scale adoption or commercial impact. For example, (Szekely et al., 2012) uses a webcam and facial analysis to allow a user to control expressive features of their synthetic speech by means of facial expressions. Such work is clearly useful, but it is difficult to assess its potential commercial impact without also knowing what proportion of currently available AAC devices include webcams and how that proportion is changing over time. Similarly, corpus based approaches such as (Mitchell and Sproat, 2012) could potentially be brought to market very quickly, but that potential can only be assessed if we also have some awareness of the range and popularity of AAC devices that either have space for such a corpus or the internet capability to access one. Unfortunately, even though there are a range of AAC focused meta-studies in the literature (see, for example, (Pennington et al., 2003; Pennington et al., 2004; Hanson et al., 2004; Alwell and Cobb, 2009)) they give little information on the technical landscape of AAC. This paper examines three issues of interest to technical researchers in AAC, each from a different stage in the research lifecycle. It then shows how the Domesday Dataset (Reddington, 2013) can provide evidence to support, or refute, assumptions, uncover important research problems, and map the technological distinctiveness of a user community.This paper is structured as follows, Section 2 introduces the Domesday Dataset and discusses the context it is used in in this work. Section 3 examines the issue that little is known about the prevalence of equipment within the AAC user community, and because of this lack of information it is difficult to establish ba...

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Electrolarynx in voice rehabilitation

Cited by 67 publications

References 32 publications

Determining an Optimal Set of Flesh Points on Tongue, Lips, and Jaw for Continuous Silent Speech Recognition

Determining an Optimal Set of Flesh Points on Tongue, Lips, and Jaw for Continuous Silent Speech Recognition

Towards an Intraoral-Based Silent Speech Restoration System for Post-laryngectomy Voice Replacement

Proceedings of the 5th Workshop on Speech and Language Processing for Assistive Technologies

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