A User-centric Design of Permanent Magnetic Articulography based

Assistive Speech Technology

Lam A. Cheah

, Jie Bai

, Jose A. Gonzalez

, Stephen R. Ell

, James M. Gilbert

Roger K. Moore

, and Phil D. Green

School of Engineering, The University of Hull, Kingston upon Hull, U.K.

Department of Computer Science, The University of Sheffield, Sheffield, U.K.

Hull and East Yorkshire Hospitals Trust, Castle Hill Hospital, Cottingham, U.K.

Keywords: Silent Speech Interface, Assistive Speech Technology, Permanent Magnetic Articulography, User-centric

Design.

Abstract: This paper addresses the design considerations and challenges faced in developing a wearable silent speech

interface (SSI) based on Permanent Magnetic Articulography (PMA). To improve its usability, a new

prototype was developed with the involvement of end users in the design process. Hence, desirable features

such as appearance, portability, ease of use and light weight were incorporated into the prototype. The

device showed a comparable performance with its predecessor, but has a much improved appearance,

portability and hardware in terms of miniaturisation and cost.

1 INTRODUCTION

The ability to communicate through speech is crucial

to humanity and plays a vital role in our social and

work life. Patients whose voice box has to be

removed because of throat cancer, trauma,

destructive throat infections or neurological

problems will inevitably lose their ability to speak.

Hence, they may experience severe impact on their

lives which can lead to social isolation and

depression (Fagan et al., 2008). However,

conventional speech restoration methods (e.g.

oesophageal speech, the electrolarynx and speech

valves) have limitations in terms of quality of speech

and usability (Fagan et al., 2008; Gilbert et al.,

2010). Furthermore, in the case of implanted speech

valves, frequent valve replacement is required within

a time span of 3-4 months, because of the growth of

biofilm coating over time (Ell et al., 1995, 1996;

Heaton and Parker, 1994).

In order to overcome these limitations, a radical

alternative approach has been introduced: silent

speech interfaces (SSIs). SSIs are devices that allow

speech communication in the absence of audible

acoustic signals. Besides their usage as

communication aid for post-laryngectomy patients,

SSIs can also be deployed in a quiet, noisy or

acoustically challenging environment (Denby et al.,

2010). Although SSIs are still in their experimental

stage, there were encouraging reports over recent

years. To date, there are several types of SSIs using

different modalities (Denby et al., 2010). Permanent

Magnetic Articulography (PMA) is a measuring

technique that captures the magnetic field variations

from a set of permanent magnets attached to the

articulators (i.e. lips and tongue) during speech

(Gilbert et al,. 2010). This does not provide explicit

information regarding the position of the attached

magnets. Rather, the measured PMA data is the

combination of the magnetic field patterns

associated to a particular articulatory gesture.

Despite the attractive attributes of SSIs, there are

still challenges in the form of processing software

(e.g. efficiency, robustness and reliable speech

generation) and hardware (e.g. portability, light

weight, unobtrusiveness and wearability).

Preliminary discussion on the influential factors of

the SSIs’ implementation had been reported by

Denby et al. (Denby et al., 2010), based upon criteria

such as ability to operate in silence and noisy

environments, usability by laryngectomee, issue of

invasiveness, market readiness and cost.

The main focus of this paper is to address the

hardware challenges facing the PMA-based SSI

system (as opposed to the speech processing

109

A. Cheah L., Bai J., A. Gonzalez J., R. Ell S., M. Gilbert J., K. Moore R. and D. Green P..

A User-centric Design of Permanent Magnetic Articulography based Assistive Speech Technology.

DOI: 10.5220/0005354601090116

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2015), pages 109-116

ISBN: 978-989-758-069-7

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

challenges which are addressed elsewhere). A

number of significant steps have been taken in order

to develop a wearable system that is appropriate for

everyday use. A novel embodiment comprising

miniaturised sensing modules and wireless headset

that is compact and comfortable is proposed.

The remainder of this paper is structured as

follow. The next section briefly overviews the PMA

technique and its development to date. Section 3

describes the system architectural design and the

challenges, followed by the performance evaluation

in section 4. The final section concludes and

provides an outlook for future research.

2 RELATED WORK

The Magnetic Voice Output Communication Aid

(MVOCA) is a PMA-based device developed within

DiSArM (Digital Speech Recovery from Articulator

Movement, www.hull.ac.uk/speech/disarm) project,

aiming to restore speech communication ability for

patients who have undergone surgical removal of the

larynx. In principal, the MVOCA consists of an

array of magnetic sensors mounted onto a

lightweight headset for detection, a set of permanent

magnets, four on the lips (ø1mm x 5mm), one at

tongue tip (ø2mm x 4mm) and one at tongue blade

(ø5mm x 1mm) as illustrated in figure 1.

Information on magnets placement was described in

(Gilbert et al., 2010). The magnets are temporarily

attached using Histoacryl surgical tissue adhesive

(Braun, Melsungen, Germany). For long term usage,

these magnets will be surgically implanted. A data

acquisition/control unit is used to condition the

acquired measurements before transmitting them to

a computer where appropriate processing and

recognition algorithms are then applied.

In previous publications (Gilbert et al., 2010;

Hofe et al., 2013b), the validity of the performance

on isolated-word and connected digits recognition

tasks using the PMA technology were shown. This

was then followed by investigation into the

performance across multiple speakers (Hofe et al.,

2013a). A feasibility study of direct speech synthesis

bypassing the intermediate recognition step was

reported in (Hofe et al., 2011). More recently,

extensive investigation into effectiveness of PMA

data in terms discriminating the voicing, place and

manner of articulation of English phones was

presented in (Gonzalez et al., 2014).

So far, the tests speech experiments were carried

out using the 1

generation MVOCA, which

consisted of five tri-axial Honeywell HMC2003

magnetic sensors, mounted on a pair of safety

glasses, as shown in figure 2. The fluctuation in

magnetic field is captured on the 15 PMA channels

and recorded onto a PC via ADLink DAQ-2206

analogue-to-digital converter (ADC), a PCI-based

card with 16-bit linear encoding. The 2

generation

MVOCA was developed and was first used in our

recent study (Gonzalez et al., 2014). Detailed

description of the latest hardware will be discussed

in the next section.

Figure 1: Placement of six magnet pellets onto lips and

tongue. The magnets are cylinders with diameter and

length of 1mm x 5mm for lips (pellets 1-4), 2mm x 4mm

for tongue tip (pellet 5) and 5mm x 1mm for tongue blade

(pellet 6).

Figure 2: MVOCA headset (1

generation) - five magnetic

sensors mounted on a frame that attached onto a pair of

safety glasses. Appearance of device when worn by user.

3 SYSTEM ARCHITECTURE

3.1 Design Consideration

Despite encouraging performance, the earlier

MVOCAs (Gilbert et al., 2010; Hofe et al., 2013a,

2013b) were not satisfactory particularly in its

appearance, comfort and ergonomic factors for the

user. Hence, to make the MVOCA more usable and

desirable, the prototype has undergone several

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iterative design cycles over the past 12 months. The

main focus during the development phase was to

consider underexplore user-inclusive requirements

by using a qualitative methodology, including

informal opinion survey, focus group and user

observation. These approaches were commonly used

in other user-centred design studies (Bright and

Conventry, 2013; Hirsch et al., 2000; Martin et al.,

2006).

Based on the discussion with the panels (i.e.

laryngectomees) of the focus group and data from

the survey questionnaires of 50 potentials users and

their families/friends, the appearance of the device is

the major factor affecting the acceptability. In fact,

other researches also indicated that the appearance is

considered a highly desirable feature for any

assistive devices (Cook and Polgar, 2008; Hirsch et

al., 2000). Six possible configurations were

presented in the survey, those resembling a

Bluetooth earpiece or a pair of spectacles were

preferred with the majority of the votes, while the

device resembling a headset microphone was

marginally acceptable by approximately 25% of the

respondents. On the other hand, devices that might

be obstructive to the mouth in anyway (in full or

partially, such as the 1

generation MVOCA as

illustrated in figure 2) were deemed unacceptable.

In addition, from previous focus group meetings

and observation studies (participants had given their

consent and the studies were approved by The

University of Hull ethics committee), valuable

feedback was gained and has greatly influenced the

creation of a user-centric design prototype. During

the prototype development, critical design questions

were raised, in term of headset appearance,

portability, weight, ease of use and cost.

Besides hardware appearance, the survey

questionnaires also identified other desirable

features, such as processing features (see table 1)

and speech quality (see table 2), by their preferred

ranking. As indicated in table 1, the quality of

reconstructed speech is highly desirable, whereas the

issue of delay between reconstructed sound and lips

movement is least prioritised. In term of speech

quality, this was further subdivided into the

characteristics listed in table 2. Both intelligibility

and naturalness of speech are considered equally

desirable, but the ability to convey emotion into the

reconstructed speech is least preferred. It should be

noted that respondents to the survey may have had

some difficulty interpreting the meaning of some of

these terms since, for instance, they may not be

aware of the extent of emotion present in normal

speech. These non-hardware related features will not

be discussed in this paper but will be addressed

separately in our future work.

3.2 New MVOCA Prototype

Major hardware components of the latest MVOCA

prototype are shown in figure 3. A set of four tri-

axial Anisotropic Magnetoresistive (AMR) magnetic

sensors (Honeywell HMC5883L), a control unit and

a power source (rechargeable 7.4V Lithium Ion

battery) were mounted on a customised headset.

Two headsets design were developed, 1) attached

onto a headband (see figure 4a), and 2) onto a pair of

spectacles (see figure 4b). The headsets (excluding

Table 1: Desirable processing features.

Software feature Description Ranking

Speech quality Measuring the quality of reconstructed speech (see also Table 2) 1

Speech mode Ability to communicate in fluent speech (ranging from isolated words to

fluent speech)

Vocabulary Size and range of words available in the database (ranging from a small

context specific vocabulary to unrestricted vocabulary)

Speaking delay Synchronisation between lips movement and synthesised voice (ranging

from speaking a complete phrase before any speech output to no delay)

Table 2: Desirable speech quality.

Speech quality Description Ranking

Intelligibility Ability to communicate intelligibly (i.e. ranging from barely intelligible

to a BBC newsreader)

Naturalness Ranging from a monotonic electronic voice to natural speech 2

Personification The choice of using own or preferred voice (ranging from another

appropriate voice to the user’s own voice)

Ability to convey emotion Ability to include emotions (ranging from no emotional content to full

emotion content)

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the pair of spectacles or headband) were fabricated

using rapid prototyping technology and their

building material were VeroWhitePlus RGD835 and

VeroBlue RGD840. A set of six Neodymium Iron

Boron (NdFeB) permanent magnets are attached

onto the lips and tongue as illustrated in figure 1.

Each magnetic sensor has three orthogonal

sensing elements to measure the three spatial

components of the magnetic field. Sensor1-3 (9

PMA channels) are used to capture magnetic field

variations caused by articulatory movements and

digitize it with 12-bit resolution. Whereas the

sensor4 is used for background cancellation, which

is for compensating the effect of earth’s magnetic

field thus enhancing the signal-to-noise (SNR) of the

desire signals.

Figure 5 shows an operational block diagram of

the 2

generation MVOCA. Each magnetic sensor

communicates to a low-power ATmega328P

microcontroller (housed inside the control unit)

through an I

C interface, samples were acquired at

100 Hz. These samples (total of 12 PMA channels)

are then transmitted to computer/tablet PC

wirelessly via Bluetooth or USB for further

processing. A bespoke graphical user interface

(GUI) had been developed in the MATLAB

environment and used mainly for on-line recognition

testing or demonstration purposes. All necessary

speech processing and recognition algorithms were

embedded into the GUI and running in the

background. If the acquired PMA signal correctly

matched an articulation gesture from the pre-stored

training dataset, thus the corresponded utterance will

be identified. A text-to-speech synthesiser is used to

generate a playback audio as an output for the

identified utterance, via an audio device (e.g.

integrated speaker of a computer).

Figure 3: Overview of the 2

generation MVOCA system,

a) MVOCA headset with b) sensor modules, c) control

unit and battery.

Figure 4: Two MVOCA headset designs were presented,

a) mounted onto headband and b) attached onto a pair

glasses. Appearances of device when worn by user.

For wireless data transmission, a class 2

Bluetooth module BTM411 (housed inside the

control unit) and USB transceiver (attached on

computer) are used. The MVOCA device will

Figure 5: Simplified MVOCA (2

generation) operational block. diagram.

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acquire its power from a battery rather than from the

computer via USB (wired mode). The average

power consumption of the current MVOCA

prototype from a 5V (regulated from 7.4V) supply is

~104 mA, which means that it can run continuously

for ~10 hours on a full charge (total 1080mAh). The

battery can be removed from the headset for

charging using a freestanding charger.

4 PERFORMANCE EVALUATION

4.1 Experimental Design

The data used in evaluating the latest MVOCA

prototype were collected from a male native English

speaker who is proficient in the usage of the

MVOCA interface. As clarified previously (Hofe et

al., 2013b), the MVOCA is a speaker-dependant

device, i.e. all associated headset measurements and

training parameters were calibrated towards

particular individual. Although inter-speaker

performance has proven possible (Hofe et al.,

2013a), the headset design and measurements would

require individually tailored for optimal

performance. In particular, the headset was

specifically designed according to the speaker

anatomy.

To evaluate the performance of the new

MVOCA prototype, a continuous speech recognition

task consisting in the identification of sequences of

English digits is chosen in this paper. This task was

chosen because the limited size of vocabulary

enables whole-word model training from relatively

sparse data and also because of the simplicity of the

language model involved. The algorithm used to

generate the random digits sequences was the one

underlying the TIDigits database of connected digits

(Leonard, 1984). The longest digit sequence consists

of seven individual digits. During the training, both

zero and oh (the two representations of 0) were

denoted as separate items.

The experimental data were collected from six

independent training sessions, i.e. two sessions each

one using different 2

generation MVOCA headsets

(figure 4a and 4b) and the remained two sessions

using the 1

generation headset (see figure 3). Each

training session consisted of five sets of data, with 4

sets (three spoken data and one mouthed data) used

as training and remained mouthed data for testing.

The idea is to mimic a scenario where the

laryngectomy patient, where his/her has intact voice

and generate training data before the laryngectomy

operation. A total of 385 utterances (5 subsets)

containing 1265 individual digits were recorded

during each training session.

4.2 Instrumental Setup

For optimal recording performance, the experiments

were conducted inside a sound-proof room, where

the audio signal was recorded with a shock-mounted

AKG C1000S condenser microphone and a

dedicated USB sound card (Lexicon Lambda). A

Matlab-based GUI was created to simultaneously

record the audio signal (sampled at 48 kHz) and

PMA data (sampled at 100 Hz). Since both data

streams were measured from separate modality,

synchronization between the two data streams was

necessary to compensate for any small deviation

from the ideal sampling frequencies of the analog-

to-digital converters (ADC). An automatic timing

alignment mechanism was used to realign both data

streams by generating start-stop markers in addition

to both audio and PMA data streams, in order to

minimised any potential timing error.

The GUI software also provided visual prompt of

the digit sequences to the speaker at regular interval

of 5 seconds during the recording session. The

measured PMA data were transferred to a PC via

USB connection. Since the speaker’s head was not

restrained, large movements could potentially distort

the recorded data and thus degrade the recognition

performance. Hence, background cancellation was

applied to compensate for any movement induced

interference against the desired PMA signals.

4.3 HMM Training and Recognition

PMA data used for speech recognition was first low-

pass filtered (i.e. removal 50 Hz noise) and

normalized as described in (Hofe et al., 2013b).

Next, the first-order time derivatives (i.e. delta

parameters) were computed for each PMA frame,

resulting in a feature vector of size 18. The second-

order derivatives (i.e. delta-delta parameters) were

not included as part of the feature vector since, as

shown in our previous works (Hofe et al., 2013a,

2013b), they did not produced significant

improvement in performance.

Then, the processed PMA data were used for

training the speech recognizer using the HTK

(Young et al., 2009). The acoustic model in the

recognizer uses whole-word Hidden Markov Models

(HMMs) with 25 states and 5 Gaussians per state

(Hofe et al., 2013b). By no means were these

parameters optimal, but the suggested parameters

settings were known for their performances from our

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previous works (Hofe et al., 2013a, 2013b). For

clarification, audio signals were not used to train the

recognizer, but only the PMA data.

4.4 Recognition Performance between

and 2

Generation MVOCAs

Word and sequence accuracy results across multiple

MVOCA devices are presented in figure 6 and 7.

The light bars relates to the condition in which only

the static PMA data is used (i.e. dimension of the

feature vector is 9), whereas the darker bars refers to

using both static and dynamic features (vector size

of 18). We will refer to these two conditions as

Sensor and SensorD features, respectively. The

results reflect the mean of the data collected on two

independent training sessions on each of the 1

and

generation MVOCA devices.

These data were analysed independently session-

by-session, and an averaged across the sessions was

produced. Merging all the data from different

session for recognition would seem a more attractive

approach, but this might lead to inconsistent

outcomes as very precise repetitive magnets

placement are required on each training session.

Nonetheless this could be overcome, as the magnets

will be surgically implanted in the final MVOCA for

long term usage. Investigations into session-

independent approach on other SSIs technique were

presented in (Maier-Hein et al., 2005) and (Wand

and Schultz 2011).

As seen in both figure 6 and 7, it is clear that

SensorD performed significantly better than using

Sensor data alone. Similar trends were also reported

in (Hofe et al., 2013b). Moreover, the results showed

a comparable performance between the 1

and 2

generation MVOCAs and in some cases the newer

MVOCA performed slightly better. Hence, this

suggests that the newer MVOCA can have better

hardware features (i.e. appearance, light weight and

mobility) but without compromising its recognition

performance by using miniaturised components (i.e.

sensors and data acquisition unit).

Figure 8 illustrates that the inclusion of mouthed

data in the training dataset improves the recognition

accuracy, particularly in terms of sequence

recognition. In this figure, the light bars relate to

mixed training data (spoken and mouthed data) and

the darker ones to non-mixed training data (spoken

only data). The results presented in figure 8 were

trained and tested using only SensorD data from the

generation MVOCA devices, as they provided

better performance as illustrated in figure 6 and 7.

Although further investigation is needed, we

recognised the importance of mixing both spoken

and mouthed data in any training session.

Figure 6: Comparison of word accuracy of connected

digits between 1

and 2

generation MVOCAs.

Figure 7: Comparison of sequence accuracy of connected

digits between 1

and 2

generation MVOCAs.

Figure 8: Comparison of training dataset (mixed or non-

mixed data) in connected digits recognition rate.

4.5 Hardware Comparison between 1

and 2

Generation MVOCAs

The greatest challenge here is to satisfy the design

objective to improve the MVOCA’s appearance,

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without compromising the device’s performance. A

summary of the key features of the latest MVOCA

system is presented in table 3.

Table 3: Hardware specifications of the MVOCA.

Specification Parameter

Sensor Module

Type Anisotropic Magnetoresistive

Dimension 12 x 12 x 3 mm

Sensitivity 440 LSb/gauss

Sampling rate 100 Hz/sensor

No. channels 12 (3 per sensor)

Control Unit

Microcontroller Low power ATmega328P

Dimension 50 x 60 x 15 mm

Operating voltage 5 V

Power source Lithium Ion battery

Headset

Material VeroBlue/VeroWhitePlus resin

Total weight 160g (including battery &

control unit)

Two versions of MVOCA headsets were

designed (see figure 4a and 4b), both headsets aim to

provide the desirable features such as light weight,

comfort and fashionable appearance as suggested by

the survey questionnaires. The current designs

significantly reduced the unattractive appearance of

the previous headset (see figure 2), this would

improve the acceptability by the end user and

ultimately improves its usage.

Significant improvements were also made in

term of the hardware miniaturisations and

portability. The previous MVOCA relied on a PCI-

based data acquisition card, thus restricted it to a

desktop PC/workstation which is highly immobile

and bulky. Although the magnetic sensors

HMC2003 are high precision sensors, they are

significant larger in size (24x45x10 mm

) and

required higher operation voltage (i.e. 12V), thus

making them non-power efficient.

In the current prototype, magnetic sensors

HMC5883L were chosen because of their

compactness, low operation voltage, low cost and

wide sensitivity range. As for signal conditioning,

low-powered microcontrollers were used. By

utilising a Bluetooth modules and a tablet PC (i.e.

mobile processing unit), the current MVOCA will be

highly portable and practical for everyday use. In

addition, the cost of the prototyping is relatively

low, as the MVOCA only utilised commercial off-

the-shelf (COTS) components. Moreover, by

shrinking the size of electronics, this inevitably

reduced the overall weight of the headset, and

making it more appealing as a wearable assistive

speech technology.

The downside of the new MVOCA prototype

would be the omission of higher precision

components (i.e. magnetic sensors) used in previous

prototype, reduction in numbers of sensors and the

use of a lower sampling rate. However, from the

results presented in figure 6 and 7, these would

suggest the concerns would be irrelevant as the

performances are comparable and slightly better in

some cases when using the 2

generation MVOCA.

There could be a couple of good explanations for

that. Firstly, the articulation movements during

speech is slow and therefore a lower sampling rate

(i.e. 100 Hz) might be sufficient. Secondly,

reduction in number of sensors was possible because

there were excess of information available from

previous MVOCA, thus some sensors can be made

redundant.

5 CONCLUSIONS

The preliminary evaluation of the new MVOCA

prototype shows comparable recognition

performances to the previous system, but providing

much desirable hardware improvements such as

portability, hardware miniaturisation, desirable

appearance and lower cost. Nevertheless, there are

still many challenges ahead before MVOCA can be

practically operated outside laboratory environments

on a day-to-day basis.

Encouraged by the results obtained so far,

extensive work is needed to create a viable wearable

assistive communication aid. Therefore, future

works include enhancing overall MVOCA

appearance, reducing power consumption and

implementing real-time features (e.g. reducing

processing time delay and utilising on-line

endpointing/segmentation). In addition, investigation

into the work on speech synthesis from PMA data

had started and preliminary results were very

encouraging.

ACKNOWLEDGEMENTS

The report is an independent research funded by the

National Institute for Health Research (NIHR)’s

Invention for Innovation Programme. The views

stated are those of the authors and not necessary

reflecting the thoughts of the sponsor.

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