Trans Jpn Soc Med Biol Eng
50(1)：38-46, 2012

CONTRIBUTION

Vowel Recognition Based on Surface Electromyography
with Electrode Grid on Submental Region
Takatomi KUBO,* Tomoki TODA,* Masaki YOSHIDA,** Takumu HATTORI,*** Kazushi IKEDA*

Abstract
This paper proposes the use of electrode grid for Japanese vowel recognition based on surface
electromyography (sEMG). Previous studies have indicated the potential effectiveness of sEMG-based speech
recognition, not only for healthy people, but also for dysarthric patients. In these studies, however, disc electrodes
or parallel bar electrodes were used and located empirically, although there exist relatively small muscles inproximity to each other in the face or neck region. In order to avoid missing out information about speech, we
examined the effectiveness of using an electrode grid, which consists of densely-spaced multielectrodes. In our
experiments, we measured sEMG signals from the submental region with the electrode grid during the production of 5 vowel sounds. Continuous hidden Markov models were applied to the sEMG signals for vowel
recognition. We compared the recognition accuracies between the two methods : One was based on signals fromall channels and the other was based on virtually reconstructed single bipolar signal. The former achieved
considerably higher recognition accuracy than the latter. This result indicates that using electrode grid is more
effective in extracting information for sEMG-based speech recognition.
Keywords : surface electromyography(sEMG), speech recognition, electrode grid, hidden Markov model(HMM).

1. Introduction
Automatic speech recognition (ASR) technology has
advanced to the point of being utilized in our daily lives.
However, ambient noise and usersʼ speech impairment
have caused low recognition accuracy. To overcome these
problems, surface electromyography (sEMG) based speech recognition has been investigated as an augmentative or alternative information source 1-12. sEMG is aprocedure that measures muscle electrical activity associated with muscle fiber contraction by using electrodes
attached on the skin. Not only in cases when a user makes
usual voiced speech, but also when voiceless mouthedspeech is made, sEMG-based speech recognition can support communication.
Over the last decade, there has been significant progress in the research on sEMG-based speech recognition.
Previous studies have indicated the potential effectiveness of sEMG-based speech recognition, not only for
healthy people, but also for dysarthric patients 3 and alaryngectomee 13. According to Duffy 14, dysarthria
is defined as “a collective name for a group of neurologic
speech disorders resulting from abnormalities in thestrength, speed, range, steadiness, tone, or accuracy of
movements required for control of the respiratory,
Presented at the Symposium on Biomedical Engineering
2011, Nagano, September 2011.
Received July 29, 2011 ; revised September 28, 2011,
November 22, 2011, November 24, 2011.
* Graduate School of Information Science, Nara Institute of
Science and Technology** Faculty of Biomedical Engineering, Osaka ElectroCommunication University*** The Hyogo Institute of Assistive Technology

phonatory, resonatory, articulatory, and prosodic aspectsof speech production”. Deng et al. 3 proposed a speech
recognition system based on sEMG, with and without
acoustic signal, wherein they showed that a high word
recognition accuracy (over 95％) could be achieved fordysarthric patients suffering from stroke and cerebral
palsy. Also, Fukuda et al. 13 proposed an sEMG-based
Japanese speech synthesizer system, where six Japanesephonemes(five vowels, i.e. /a/, /i/, /u/, /e/, /o/, and one
syllabic nasal /n/) were recognized from the patterns ofsEMG signals using a probabilistic neural network, andthen words were recognized from series of phonemes
using hidden Markov models (HMM). Although the recognition accuracy of continuous speech production was
lower than that of syllable-wise speech production, effective phoneme recognition with a laryngectomee wasachieved by Fukudaʼs system. These results indicate thatsEMG-based speech recognition has the potential to be anovel type of speech prosthesis.
To achieve a high recognition accuracy in sEMG-based
speech recognition, it is necessary to decide the appropriate location of the electrodes. However, in previous
studies, disc electrodes or parallel bar electrodes wereused and located empirically, based on anatomical knowledge. Because there exist relatively small muscles inproximity to each other in the face or neck region, it isdifficult to avoid the influence of cross talks and endplatezones when conventional measurement methods areapplied.
Lapatki et al. 15 proposed a high density multichannel sEMG system using electrode grid to improve signalto-noise ratio of sEMG signals recorded from the lower
facial muscles. To avoid missing out information about
speech in the measurement step, we examined the effectiveness of using an electrode grid, which consists of

Takatomi KUBO, et al : Vowel Recognition Based on Surface Electromyography with Electrode Griddensely-spaced multielectrodes. In our experiment, wemeasured the sEMG signals of the submental region withthe electrode grid during the production of 5 vowel
sounds.
There are multiple muscles in the submental region
that play important roles in controlling the movements ofthe mandible and tongue 16. The anterior belly of thedigastric, the mylohyoideus, and the geniohyoideus act topull the hyoid bone upward and forward, or to depress themandible. The anterior genioglossus is responsible for pulling the dorsum forward and downward. The posterior
genioglossus has the function of pulling the tongue root
forward and raising the dorsum. Thus, these muscles manipulate tongue shape, which is relevant with the vowel
production.
However, the submental region was not given muchemphasis in previous studies, and only one or at most twochannels were used in these experiments. Their function
should receive considerably more attention for speech recognition, especially for vowel recognition.
To investigate whether electrode grid is effective in
extracting information for sEMG-based speech recognition, we compared the recognition accuracies between
two methods : One was based on signals from all channels(hereafter, “all-channel method”) and the other was basedon virtually reconstructed single bipolar signal (“singlechannel method”).

Fig. 1（39）( a ) The electrode grid. The white double-headed
arrow indicates the row direction, and the graydouble-headed arrow indicates the column direction.
( b ), ( c ) The location of the electrode grid on thesubmental region : lateral view ( b ) and frontal view( c ).

2. Method
2･1 Surface EMG System
We used an sEMG system developed by Hattori et al.
with few modifications made on the electrode grid17.
The electrodes which consisted of silver bars in Hattoriʼsstudy were substituted with spring connector pins (SK
KOHKI Co., Ltd., AX-12ENR-00), with each pin having a
diameter of 0.8 mm, to absorb any dynamic displacementof the attached site (Fig. 1). The set of electrodes werearranged in an array of 8 rows by 8 columns, with theinterelectrode distance (IED) set to 5.08 mm, from centerto center, in both directions. To reduce skin impedance, avoltage follower circuit was built with each electrode.
The electric potential differences between each pair ofelectrodes neighboring in column direction were amplified up to 66 dB with band-pass filtering between 10 to1500 Hz. Subsequently, the electric potential differenceswere digitized with a 16-bit analog-to-digital converter(National Instruments, NI USB-6255) and a laptop computer running MATLAB with its Data Acquisition
Toolbox (MathWorks, 2010a). A microphone(KNOWLES,
SP0103 NC3-3) was also attached in front of the electrodegrid, so that acoustic signal could be simultaneously
recorded with the sEMG signals.
2･2 Data Collection
Two adult Japanese native speakers were recruited as
participants (1 female and 1 male with mean age of 33
years) . Both of the participants had no known speech
impairment. In each trial, the participants were asked to
produce each of the five Japanese vowels (/a/, /i/, /u/,/e/, and /o/) once in a random order. The task vowels

were presented on a screen for 1 second with an intervalof 2 seconds between each of them, and the participants
were instructed to start vowel production at the onset ofthe visual presentation and stop at the offset. Subject 1
conducted 50 trials in one day, while Subject 2 conducted
50 trials divided in half over two days. Subject 1 took 7
rest intervals throughout the experiment. Every time
Subject 1 wanted to take a rest, enough time was given,
while the electrode grid was removed. Though Subject 2
did not want to rest, the subject was allowed to remove
the electrode grid whenever he wanted.
During vowel production, the sEMG signals wererecorded with the electrode grid attached on the submental region as shown in Fig. 1 ( b ), ( c ) . The gridʼscenterline in the column direction and the last row werealigned with the center of the mandible and the posterioredge of the submental triangle, respectively, by visualinspection. Figure 2 shows the muscles in the relatively
superficial layer of the submental region and the corresponding positions of the electrodes. The anterior
bellies of digastrics produce the sEMG signals whose
amplitudes are relatively large. Therefore, the sEMG
signals not only from the mylohyoideus but also frommuscles in deeper layers, e.g. the geniohyoideus and thegenioglossus, tend to be masked. In addition, there areendplate zones near the center of each muscle. Although
the endplate zones and cross talks should be taken into
account to avoid deterioration of the signal-to-noise ratio,
it seems to be rather difficult to find appropriate locations
using conventional bipolar electrodes whose diameters or（40）Fig. 2

Trans Jpn Soc Med Biol Eng. Vol. 50, No. 1, 2012

Anatomy of muscles in the submental region reprinted from 18 and corresponding positions of theelectrodes. Black dots represent the positions of theelectrodes.
“abd” : the anterior belly of the digastric“mh” : the mylohyoideus“sm” : seam of the mylohyoideus (raphe of the mylohyoideus)

lengths are approximately 1 cm.
As preparation, the skin on the submental region was
cleaned with an alcohol swab prior to attaching the electrode grid. The electrode grid was fixed to a tripod, andthe participants grasped the tripodʼs legs, wrapped withstainless sheets, which served as the ground reference.
Both the sEMG and acoustic signals were then captured
and digitized at 16 kHz with an analog-to-digital converter.
Written informed consents were obtained from theparticipants prior to the experiments. This study was approved by the institutional ethics committee of Nara
Institute of Science and Technology.
2･3 Data Preprocessing
The sEMG signals were filtered with an 8th order lowpass Butterworth filter having a cut-off frequency of 500
Hz, and then downsampled to 2 kHz. Examples of thesignals coming from each vowel produced by Subject 1
are illustrated in Fig. 3 and 4. The signals coming fromthe anterior part seem to indicate similar patterns. In fact,hierarchical clustering, which was conducted according
to Euclidean distances of the normalized signals, revealed
that not only the anterior part but also the middle andposterior parts were clustered. The dendrogram of thishierarchical clustering is shown in Fig. 5. In addition, correlations between the representative channels and thewhole channels during production of/o/are shown in Fig.
6 and 7. The representative channels consist of the channels between the 3rd and 4th rows in the 6th column andbetween the 7th and 8th rows in the 7th column. Hereafter, these channels are denoted as “3-4, 6” and “7-8, 7”.
While the channels “3-4, 6” and “7-8, 7” have positive
correlations with the surrounding channels, these twochannels have a significant negative correlation (−0.533,
p ＜ 5.0×10−291) with each other. This phenomenon might
be relevant with the endplate zones of the anterior belliesof the digastrics from where the propagation of the motor

unit action potentials starts.
In the vowel recognition process, the onsets and offsetsof the acoustic signals were used as reference to determine those of the sEMG signals. The criteria applied in
detecting the onsets and offsets of the acoustic signals
were based on a set of amplitude thresholds, and thesesignals were then visually confirmed and corrected manually in the two cases. With the consideration of the delay
between the sEMG signals and the acoustic signals 5,
the onset of the sEMG signals were set to precede that of
the acoustic signals by 150 msec, although the restingstate could also be included. As for the offsets of sEMG
signals, these were set to 150 msec after the offsets of theacoustic signals. These onsets and offsets of the sEMG
signals were used to extract data for the following feature
extraction process.
2･4 Feature Extraction
To be able to compare the single-channel method withthe all-channel method, bipolar signals from all possiblecombinations of electrodes within the same column werevirtually reconstructed from original signals, by adding
signals from all channels between the two selected electrodes. Two types of feature sets were used in this study :( 1 ) time domain features, and ( 2 ) cepstral coefficients.
Features were extracted from the windowed signals ofeach channel. The window length was set to 25 msec with50 samples, while the window period was set to 12.5 msec
with 25 samples.
The time domain features consisted of the average
rectified value (ARV), root mean square (RMS) zerocrossing rate of high-pass filtered signals, and the mean of
the raw signals, along with the Δ and ΔΔ features of thesefour features. To some extent, these features were similar
to the features proposed by Jou et al. 4 except that lesscontextual information was used.
The cepstral coefficients, cn, were calculated as :cn=F −1 log  X(f)
for each channel after Hamming window was applied tothe signals. X(f) represents the short-time frequency
spectrum, while F −1 indicates the inverse Fast Fouriertransform. The real parts of the lower 15 cepstral coefficients, including the 0th coefficients, were used asfeatures, along with the Δ and ΔΔ features. Several researches have shown that Mel-frequency cepstral coefficients (MFCC), which are derived from cepstral coefficients by applying filter bank based on the Mel-scale, canalso be used as features 3, 12 . However, there is no
physiological plausibility to use MFCCs to parameterizesEMG signals, since the Mel-filter bank is designed toapproximate human auditory perceptual response to
acoustic signals. In addition, if the sampling rate, window
length used in this study, and spectral features of thesEMG signals are taken into account, then the usefulnessof Mel-filter bank will be reduced. Therefore, in this study,
we employed cepstral coefficients instead of MFCC. For
the all-channel method, features from all 56 channels wereconcatenated. This concatenation resulted in having morethan several hundreds of dimensional features.
Dimensionality reduction was performed using linear

Takatomi KUBO, et al : Vowel Recognition Based on Surface Electromyography with Electrode Grid

Fig. 3（41）

The waveform of the sEMG signals. ( a ), ( b ), and ( c ) denote the signals during production of /a/, /i/, and /u/,respectively. Waveforms of the 9th column indicate the corresponding acoustic signals.

（42）Fig. 4

Fig. 5

Trans Jpn Soc Med Biol Eng. Vol. 50, No. 1, 2012

The waveform of the sEMG signals. ( a ) and ( b ) denote the signals during production of /e/ and /o/, respectively.

The dendrogram of hierarchical clustering on channels. Each label denotes the row and column of the channel, for example,“3, 6” denotes a channel between the 3rd and 4th electrodes in the row direction within the 6th column.

Takatomi KUBO, et al : Vowel Recognition Based on Surface Electromyography with Electrode Grid

（43）

produce the recognition accuracy.

3. Results

Fig. 6

Correlations between the signal coming from channel“3-4, 6” and those coming from all channels of theelectrode grid.

Fig. 7

Correlations between the signal coming from channel“7-8, 7” and those coming from all channels of theelectrode grid.

discriminant analysis (LDA), which is commonly used tomap the data onto a lower dimensional subspace keeping
discriminative information as much as possible. The
resulting final dimensions were reduced to 4 in bothmethods.
2･5 Vowel Recognition
Continuous HMM was adopted for vowel modeling. An
HMM represents a stochastic process that takes sequential data as the inputs, and outputs the probabilities thatthe data are generated by the model. For each vowel, we
used a 9 state left-to-right HMM with 3 Gaussian mixtures,
whose covariance matrices in each state are diagonal.
Expectation maximization (EM) algorithm 19 was utilized in parameter estimation, and the word with the
maximum likelihood was adopted as the recognition result. Hidden Markov Model Toolbox 20 was used to
implement the HMMs in this experiment. A 5-fold crossvalidation was conducted to investigate the recognition
accuracy. In the 5-fold cross-validation, the data is partitioned into 5 groups. A single group of data is retained as
the validation data for testing the model, while theremaining 4 groups of data are used as training data forthe models. This procedure is repeated 5 times, with eachof the 5 groups used exactly once as the test data. Thenthe 5 results obtained by the repetition are averaged to

The comparison between the recognition accuracies ofthe different channels and feature conditions are shown in
Fig. 8. For the single-channel method, the best recognition
accuracies between all possible electrode combinations
are indicated. The all-channel method outperformed thesingle-channel method. With respect to features, using
cepstral coefficients indicated higher recognition accuracies than using time domain features. The all-channel
method with cepstral coefficients achieved 85.6％ recognition accuracy for Subject 1 and 79.6％ recognition accuracy for Subject 2.
Fig. 9 depicts the differences of recognition accuracies
between the used electrode locations in the singlechannel method, including the locations which were usedin Fig. 8. Fig. 9( a )shows the result of the case when theparticipant was Subject 1 and the IED was 15.24 mm. The“Row”and“Column”labels denote the positions where thevirtual bipolar electrodes in the grid were selected. In thefollowing, location (i-j, k) denotes the bipolar signal between two (row, column) positions : (i, k) and (j, k) . In
Fig. 9( a ), the recognition accuracy reaches a maximumof 51.6％, at location (5-8, 2). However, it can be seen that
(4-7, 1), (4-7, 2), and (4-7, 3), which are neighbors of the
maximum point, indicate accuracies of 38.0％, 30.8％, and38.4％, respectively. In some parts of the central locations
in rows “2-5” or “3-6”, the accuracies are at 20 to 30％. In
Fig. 9( b ), the location of the highest recognition accuracy is different from that of Fig. 9( a ). Yet some parts ofthe central locations in the row direction still indicatedaccuracies in the range of 20 to 30％. In Fig. 9 ( c ),tendencies of Subject 2 are shown. Here, the locations(3-5, 3) and (3-5, 6) reach the recognition accuracies of
54.0％ and 53.6％, respectively. However, the location(3-5,7)which is a neighbor of the location (3-5, 6) indicates anaccuracy of 28.4％. In some posterior locations, the accuracies are at 20 to 30％.
Tables 1 and 2 illustrate the confusion matrices, in the
all-channel method and cepstral coefficients of Subject 1
and 2, respectively. The rows of the tables represent theactual spoken vowels, while the columns represent thevowels recognized by the HMMs. In both participants,
there is a relatively high tendency that vowels /a/ and /e/cannot be discriminated from each other.

4. Discussion
It has been confirmed in this experiment that the allchannel method has achieved considerably higher recognition accuracies for the 5 Japanese vowels than the
single-channel method, although the oblique and lateral
directions have not been investigated in this study. This
result indicates that using electrode grid is more effective
in extracting information for sEMG-based speech recognition than using conventional electrodes.
As shown in Fig. 9, the single-channel method is
influenced by the locations of the selected electrodes. In
addition, inter-individual variability is also shown in Fig.

（44）Fig. 8Trans Jpn Soc Med Biol Eng. Vol. 50, No. 1, 2012

Comparison of the recognition accuracies for conditions with different features and channels used. All ch :all-channel method, Single ch : single-channel method,
Ceps : cepstral coefficients, TD : time domain features.

9. Therefore, when conventional disc or parallel bar electrodes are used, it is highly important to carefully
consider those locations with respect to each subject
might be required in order to achieve higher recognition
accuracies. But, doing such tests for each and every
subject seems to be rather impractical. One of the reasonsfor this inter-individual variability is that there are differences in anatomical structures and muscular coordination patterns. To take into account the anatomical structure, magnetic resonance imaging (MRI) of the lower
position of the face and neck should be useful 21.
From the confusion matrices, shown in Tables 1 and 2,
there is a possibility that vowels /a/ and /e/ cannot be
discriminated with high accuracy from each other when
only sEMG signals from the submental region are used.
This finding is consistent with another previous study. By
using three parallel bar electrodes, Manabe et al. 8
conducted an experiment of Japanese vowel recognition
based on sEMG signals measured from the orbicularis
oris, the zygomaticus major, and the anterior belly of thedigastric during mouthed speech. However, only the RMS
values of the signals were used as features. Although
there seemed to be difficulty in vowel recognition using
the RMS value from the anterior belly of the digastric, theRMS value from the orbicularis oris could contributesignificantly to the discrimination of the vowels /a/ and/e/. Indeed, there is a difference in the condition of theusual voiced speech and voiceless mouthed speech between our experiment and that of Manabe et al. But theirexperiment implies that additional measurement from theorbicularis oris can improve the recognition accuracy ofour proposed method in discriminating between vowels/a/ and /e/. Moreover, consonants should also be considered in future studies. In order to achieve it, sEMG signals
from other perioral muscles must be considered as well.
On the other hand, the tradeoff for the dense measurement given by an electrode grid is that it may containsignal redundancy. It is therefore necessary to reduce thisredundancy, considering the spatial inter-individual variability as well, especially when working with dysarthricpatients. To this end, experiments must be conductedwith more subjects.

Fig. 9Changes of recognition accuracies with electrodelocations under the single-channel method. ( a ) to( c ) indicate the conditions, consisting of the subject
and the IED, which is described as the following :( a ) Subject 1 with IED ＝ 15.24 mm.
( b ) Subject 1 with IED ＝ 20.32 mm.
( c ) Subject 2 with IED ＝ 10.16 mm.
The “Row” and “Column” labels denote the rows andcolumns of the electrode grid from where virtual
bipolar electrodes were selected.

5. Conclusion
This study proposed the use of an electrode grid forJapanese vowel recognition based on surface electromyography (sEMG) . We compared the recognition accuracies of 5 Japanese vowels between two methods : the allchannel method which used an electrode grid, and theTakatomi KUBO, et al : Vowel Recognition Based on Surface Electromyography with Electrode Grid
5.

6.
7.

8.

9.

10.

single-channel method which used a virtually reconstructed single bipolar signal. The former achieved recognition accuracies of approximately 80 to 85％, which was
higher than that of the latter. This result indicates thatusing an electrode grid is more effective in extracting information for sEMG-based speech recognition than using
a conventional disc or parallel bar electrode. Furthermore, future works on obtaining the findings for spatial
inter-individual variability of sEMG signals and reducing
the redundant electrodes are warranted.

Acknowledgement
This study is supported in part by a research grantfrom the Foundation for Nara Institute of Science andTechnology. We would also like to thank Dr. Wesley
Norman for giving us his kind permission to reprint theanatomical image in Fig. 2.
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（46）Trans Jpn Soc Med Biol Eng. Vol. 50, No. 1, 2012
Takumu HATTORI
Takumu HATTORI received his Dr.Eng.

Takatomi KUBO
Takatomi KUBO received his B.M. degreefrom Osaka University, Japan, in 2002, andhas 5 years of medical experience in neurology. He completed a Human Resource Development Program for Medical Device Development Coordinators organized by Kobe University and Kyoto University, Japan, in 2010. He is currently aPh. D. student at the Graduate School of Information Science,
Nara Institute of Science and Technology. His research interests include surface electromyography based speech recognition, brain-computer interface, and developing a communication device for dysarthric patients.
Tomoki TODA
Tomoki TODA received his B.E. degreefrom Nagoya University, Aichi, Japan, in
1999 and his M.E. and D.E. degrees from the
Graduate School of Information Science,
Nara Institute of Science and Technology（NAIST), Nara, Japan, in 2001 and 2003, respectively. He was a Research Fellow of JSPS in the Graduate
School of Engineering, Nagoya Institute of Technology, Aichi,
Japan, from 2003 to 2005. He was an Assistant Professor of the
Graduate School of Information Science, NAIST from 2005 to
2011, where he is currently an Associate Professor. His research interests include statistical approaches to speech processing such as voice transformation, speech synthesis, speech
analysis, speech production, and speech recognition.
Masaki YOSHIDA
Masaki YOSHIDA received his B.S., M.S.,
and Ph.D. degrees, all in Electrical Engineering from Osaka University, Japan, in 1976,
1978, and 1995, respectively. Following a
stint as Lecturer at School of Allied Medical
Sciences, Kobe University in 1984, he wasappointed Associate Professor at the Faculty of Engineering,
Kobe University. In 1998, he was appointed Professor at theFaculty of Engineering, Osaka Electro-Communication University. He received Japanese Society for Medical andBiological Engineering Award in 1983. He is currently aProfessor of Faculty of Biomedical Engineering, Osaka Electrocommunication University. His current research interests
include signal processing and analyses of EMG signals, andmyoelectric prosthetic hand.

degree from Nara Institute of Science andTechnology, Nara, Japan in 2009. He is currently a researcher at the Hyogo Institute ofAssistive Technology. His research interests
include processing of biological signals obtained from multichannel electrodes, quantification of postureduring swallowing, and development of input devices for thephysically disabled person. He is a member of Japanese Society
for Medical and Biological Engineering and IEEJ.
Kazushi IKEDA
Kazushi IKEDA received his B.E., M.E., andPh.D. degrees from the University of Tokyo,
Japan, in 1989, 1991, and 1994, respectively.
In 1994, he belonged to Kanazawa University
and joined the faculty of Kyoto University in
1998. He has been a Professor of Graduate
School of Information Science, Nara Institute of Science andTechnology since 2008. His research interests include machinelearning, brain informatics and adaptive systems. He is theeditor-in-chief of Journal of JNNS and an associate editor ofIEEE Trans. Neural Networks, Neural Networks and IEICE
Trans. Information and Systems.