Sensors and Materials, Vol. 26, No. 7 (2014) 519–528
MYU Tokyo
S & M 1018

Expiration Training System for Treatmentof People with Speech Problems
Yasuhiro Ogoshi*, Sakiko Ogoshi1, Kyoko Yamazaki2, Tomohiro Takezawa3,
Akio Nakai4,5, Kiyoharu Tsuji6 and Yoshinori Mitsuhashi7
Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan
1Career Design Laboratory for Gender Equality, Kanazawa University,
Kakuma-machi, Kanazawa 920-1192, Japan
2Fukui College of Health Sciences, 55-13-1 Egamicho, Fukui 910-3190, Japan
3The National Institute of Vocational Rehabilitation,
3-1-3 Wakaba, Mihama-ku, Chiba 261-0014, Japan
4Hyogo Children’s Sleep and Development Medical Research Center,
1070 Akebono-cho, Nishi-ku, Kobe 651-2181, Japan
5Department of Child and Adolescent Psychological Medicine, University of Fukui Hospital,
Matsuoka-shimoaizuki, Eiheiji-cho, Yoshida-gun, Fukui 910-1193, Japan
6Fukui Prefectural Rehabilitation Center for Children with Disabilities,
2-8-1 Yotsui, Fukui 910-0846, Japan
7Faculty of Education and Regional Studies, University of Fukui,
3-9-1 Bunkyo, Fukui 910-8507, Japan(Received January 14, 2014; accepted April 30, 2014)
Key word: speech therapist, motor speech disorders, articulation disorders, rehabilitation,
expiration training, air flow sensor

For a speech therapist, the breathing method is one important technique for training
people with speaking difficulties. In the past, a trainer used the so-called “blowing
technique” or “breathing play” for speech-related expiration training. However,insufficient numbers of therapists make it difficult to widely apply these breath-training
methods, and the development of new breath training methods has become mandatory.
Therefore, a new training system, in which the expiration pattern of a speech therapist isanalyzed using an airflow sensor and converted into a graphic visual image to facilitate
the easy recognition of expiration patterns, was developed. Experimentally, using thissystem with nonhandicapped trainees demonstrated that such trainees could successfullyand easily imitate the trainer’s pronunciation. It is suggested that people with speech
problems would be able to easily learn appropriate speech-related expiration patterns byusing the visual patterns produced by this system.

Corresponding author: e-mail: y-ogoshi@u-fukui.ac.jp

*

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1.

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Introduction

Various training methods have been implemented by speech therapists for people
with speech, language and hearing disorders.(1–4) Among them, expiration training is
considered highly important for improving motor functions of articulatory organs. One
expiration training method is the “blowing technique”, in which a trainee blows air
through a straw placed at a depth of 5–10 cm in a cup of water. The expiration power
necessary for making an utterance (5–10 cm H2O) and duration (5 s) are verified, andthen the strength of respiratory muscles is increased accordingly.(5,6)
Various pronunciation techniques giving visual feedback of breath control have been
conducted for pronunciation training. These include blowing at a candle (rhythmically,
alternating between strong and weak blows, so as not to put out the candle) and blowing
feather flutes, party horns, paper balloons, blowing balls, ping-pong balls, and pinwheels,among others.(7) For vocalizing continuous sounds (smoothly shifting from one single
sound to another), blowing that can be visually confirmed is considered effective(8)because continuous long expirations are required.
We have developed a system in which trainees can imitate therapists’ expiration
patterns (e.g., timing, strength, and length of expiration) by visually examining thepatterns in a graph shown through a computer. In this method, a trainee holds amouthpiece with a chosen shape and simultaneously watches a graph indicating his/her
expiration patterns.(9,10) It was confirmed that training using this system was effective fordecreasing the differences in expiration patterns between a therapist and a trainee, whichwere calculated from the graph.

2.

Suggested System

2.1

Structure of system

The structure of the system is shown in Fig. 1, expiration training reported in thispaper was conducted with this system. The system includes various elements such

Fig. 1. Basic structure of training system.

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as CCD camera input, voice output, and mike input, among others, and can deal withvarious symptoms of people with speech, language and hearing disorders (i.e., trainees).

2.2

Measurement of volume of expiratory flow

2.3

Example of expiration training

The device for measuring the expiratory flow rate is shown in Fig. 2. The device
consists of the following sensor units and an A/D converter. The data from the unit is fed
into a computer.
∙ Air flow sensor 30–1000 (ml/s): FD-A1, Keyence∙ Amplifier unit: FD-V40A, Keyence∙ A/D converter 16 (bit)・1MSPS: CSI-320416, Interface
In this system, the sampling period is set as 10 (ms) and the resolution of the A/D
converter is set at 12 (bit).
Figure 3 is a graph showing the expiration data of a healthy male adult while blowing
rhythmically as measured using the air flow sensor.
The computer menu used in the expiration training system is shown in Fig. 4. As
shown in Fig. 4, the training menu is set according to the following procedure:Fig. 2. Device for measuring expiratory flow rate in expiration training system.

Fig. 3. Signal of airflow sensor.

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Setting(1) Trainee(2) Choose the word you want to train.
(3) Levels of training (Level 1, 2, or 3)(4) Frequency of training(5) EnterFig. 4. (Color online) Top menu of training system.

(1) Select the trainee’s number (select the ID and name of the trainee from the pull-downmenu).
(2) Choose the shape of the mouth (e.g., “a” or “u”) the trainee wants to train.
(3) Choose the level of training (the degree of difficulty increases in ascending orderfrom Level 1 to Level 3).
(4) Select the number of times the trainee has received the training (automatic selection
is possible).
(5) Click the “Enter” button and go to the training page.
The training page is shown in Fig. 5. The expiration pattern of the therapist isindicated in the upper part and that of the trainee in the lower part of the figure.
The strength of expiration is discretely expressed as zero, weak, medium, or strong.
The model expiration is indicated as a simple pattern because when changes in thevolume of expiratory flow are shown in a graph, such as that in Fig. 3, it is difficult fortrainees to imitate the pattern. Therefore, a simple graph was adopted in this study.
In Fig. 5, the thresholds of a mouth shaped “u” were set as 100, 400, and 700 (ml/s).
When values measured by using the airflow sensor exceed the above thresholds, the levelof expiratory flow is judged as weak, medium, or strong, respectively. These thresholds
are adjusted on the basis of the trainees’ condition, so that they can easily achieve the set
goal, and motivation for training would increase.
Thresholds were determined by measuring the maximum expiratory flow Xmax ofthe trainee before starting training. Strong was then defined as more than 70% of Xmax,
medium as more than 40% of Xmax, and weak as 10% of Xmax or more.
The therapist’s expiration pattern is decided in advance, such that its length is 10 s.
During the training, the indicator (vertical line) moves from left to right with the courseof time. Trainees control the timing, strength, and length of their breath by checking thegraph and the indicator.

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Fig. 5. Example of practice session.

As shown in Fig. 3, the performance characteristics of the sensor are such that
there is an approximately 0.1 s delay at the start and finish of expiration. Therefore,
assessment is not conducted during periods of sudden changes, such as at the start or endof expiration. The strength of the breath is assessed after sudden changes have ended.

2.4

Expiration training menus

2.5

Evaluation of expiration training

Three levels of a therapist’s expiration patterns are available, such that the trainees
can choose an appropriate level.
As training advances, a therapist’s expiration pattern becomes more refined withincreases in the complexity of the interplay of changes in breath intensity and duration.
A trainee proceeds to the next more complex level upon attaining advanced scores in theprevious lower level of training.
The volume of expiratory flow differs on the basis of pronunciation using different
mouth shapes. In this study, mouthpieces for the mouth shapes of “a” and “u” wereprepared. Trainees choose either one of these and put on the mouthpiece corresponding
to the selected pronunciation.
Evaluation was conducted by calculating the score based on the differences in
expiration patterns between the therapist and the trainee. Lower scores indicated better
results.
The score was calculated using eq. (1): s = step time, {1,…,1000}; T(s) = strength oftherapist’s expiration, {0,1,2,3}; P(s) = strength of trainee’s expiration, {0,1,2,3}.
Score =1000
s=1

|T (s) − P(s)|

(1)

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Other formulas used were as follows. First, the rate of concordance (%) between thetherapist’s data and trainee’s data was calculated using eq. (2).
Concordance rate = 1 −

Score
× 100
3000

(2)

Next, the gap of timing expiration (difference in mean time of starting position in ms)
was calculated using eq. (3): n = number of times of therapist’s expiration, {1,2,3,…,N};TS(n) (ms)=initiation time of each n with therapist; PS(n) (ms)= initiation time of each nwith trainee.
Difference in mean time of starting position =N|T S (n) − PS (n)|

(3)

n=1

The example shown in Fig. 5 indicates N = 4.
Furthermore, the ability to control breath strength was calculated with the volume
of expiratory flow (ml/s), which was measured using the sensor. Identical to eq. (1), s= step time, {1,…,1000} and T(s) = strength of therapist’s expiration, {0,1,2,3}. The
volume of expiratory flow of the trainee was expressed as PF(s) = {0–999}. Mean
expiration levels that were over the target value (mean of over air flow (ml/s)) and thosethat were under the value (mean of under air flow (ml/s)) were calculated with eqs. (4)and (5). The counts = {1,2,3,…} were the frequencies of expiration levels that were overor under the target value among all the samples (N=1000).
Over air f low =1000|T S (n) − PS (n)| (if T S (n) > PS (n), Count ← Count + 1) (4)s=1

Mean of over air flow =Over air flow
Count

(5)

Regarding the mean of under air flow, (if TS(n) > PS(n), Count ← Count + 1) in eq. (4)was replaced by (if TS(n) < PS(n), Count ← Count + 1).
The formulas shown above are useful for conducting evaluations based on different
characteristics of trainees, by preparing various evaluation indexes. Results ofcalculations using eq. (1) are shown in the experimental results described below.
Furthermore, at each training session, not only the scores calculated with theformulas, but also the raw data of the expiratory flow were recorded, such that a future
reevaluation would be possible. Recording with a time series facilitated confirming theeffect of training.

3.

Experimental Methods

Changes in training scores of healthy male adults (N = 5, A–E) using Level 1
therapist’s data shown in Fig. 5 are displayed in Fig. 6. Each trainee conducted 10
training sessions.

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Fig. 6. Learning curve of expiration training.

Equations (2)–(5) were employed to derive scores on the vertical axis of Fig. 6.
These scores represent differences between values presented by the therapist and values
attained by the trainee with smaller scores representing improved results on the part of
the trainee. As indicated in Fig. 6, improved scores serve to confirm progress throughthe step-wise training.
The feedback provided to the trainee is thought to better inform their adjustments
in breath control (timing, strength, and length of expiration). In addition, successful
reproduction of utterances requires repetitive training and the gamelike feedback in theform of a score may improve the level of engagement of the trainee.

4.

Conclusions

The system developed in this study is a prototype and was tested with healthy adults.
The results indicated an improvement of the score as the learning steps proceeded.
Therefore, this system was considered effective for expiration training. However, longterm observations are necessary for confirming the improvement of expiration ability. It
is suggested that in the future, this system should be tested and evaluated with people
that actually require expiration training. Furthermore, adjustment of the airflow sensor(i.e., setting the thresholds for the strength of expiration) and the expiration patternof therapists should be examined, so that trainees are able to more easily undergo thetraining.
Additionally, this system could be used as a method to confirm the rehabilitative
effects of training for strengthening respiration muscles, such as “stretching to expand themobility range of the lungs” and “training for strengthening muscles around the lungs”,
among others. Furthermore, the addition of test items assessing basic capacities, suchas lung capacity and volume of expiratory flow, to training items suggested in this study(e.g., timing and control of the length and strength of blowing), would also facilitate theconfirmation of the effects of rehabilitation.

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Recently, the aging population has become a serious problem in Japan, andmaintaining the health of the elderly has become important. It is suggested that inaddition to providing speech training for people with disabilities, this system could be
used to address new needs, such as speech rehabilitation and anti-aging training for theelderly.

Acknowledgements
This work was supported by KAKENHI (Grant-in-Aid for Scientific Research(C) Nos. 21610007 and 24600005, Grant-in-Aid for JSPS Fellows No. 23-40189, andResearch Activity Start-up No. 22830033).

References
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Speech Disorders in Children and Adults, 2nd Ed. (C.V. Mosby, St. Louis, 1999).
3 N. Kurauchi (ed.): Gengo Chokaku Shogai Soron I (Kenpakusha, Tokyo, 2009).
4 K. Yamazaki (ed.): Gengo Chokaku Shogai Soron II (Kenpakusha, Tokyo, 2009).
5 Ed. M. Ito and S. Sasanuma: Manual for Speech-Language Therapy (Ishiyaku Publishers,Inc., Tokyo, 2002).
6 M. Nishio: Assessment of Motor Speech for dysarthria (Interuna Publishers, Inc., Tokyo,
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Corp., Tokyo, 2004).
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About the Authors
Yasuhiro Ogoshi is an associate professor of the Graduate Schoolof Engineering, University of Fukui, Japan. He received his
Ph.D. Degree in Engineering from the University of Kanazawa in
2001. He is engaged in studies related to human interface.

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Sakiko Ogoshi is an assistant professor of the Career Design
Laboratory for Gender Equality, Kanazawa University, Japan.
She received her Ph.D. Degree in Engineering from the
University of Kanazawa in 2004. She was a researcher at theJapan Society for the Promotion of Science and the Facultyof Education and Regional Studies, University of Fukui from2011 to 2014. Her research interests include special needs anddevelopmental disorders and knowledge management systemapplied information technology.
Kyoko Yamazaki is a professor of the Speech-Language-Hearing
Therapy at the Department of Rehabilitation in Fukui College of
Health Sciences. She completed her doctoral program without
a doctoral degree in Special Education, Graduate School of
Education, Tokyo University of Education in 1978. Since 1984,
she has educated the students in the Speech-Language-Hearing
Therapy Course, as well as counseled and cured the children withdelayed development of language.

Tomohiro Takezawa received his Ph.D. Degree in Psychology
from Hiroshima University. He specializes in cognitive abilities
and assistive technology.

Akio Nakai, is currently the Senior Executive Vice Director of
Hyogo Children’s Sleep and Development Medical ResearchCenter and a visiting professor of the Department of Childand Adolescent Psychological Medicine, University of Fukui
Hospital, Japan. He received his M.D. in 1986 and Ph.D. in1991 from Fukui Medical University. He is both a neurodevelopmental and behavioral pediatrician and a clinicaldevelopmental psychologist. He studied brain imaging ofserotonin synthesis in McConnell Brain Imaging Centre,
Montreal Neurological Institute, McGill University, Canada
from 1998 to 2000. He is the Director of the Japanese Societies

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Sensors and Materials, Vol. 26, No. 7 (2014)of Baby Science, the Japanese Society of Child Science, and the Japanese Society ofDevelopmental Neuroscience. He is also the Editor-in-Chief of Baby Science andthe Councilor of The Japanese Society of Child Neurology. He is working on several
international collaborative studies of Developmental Coordination Disorder (DCD) with
Canada, the Netherlands, Israel, UK, and Vietnam, as a representative of Japan in theInternational Society for Research into DCD.
Kiyoharu Tsuji is a physical therapist of the Fukui Prefectural
Rehabilitation Center for Children with Disabilities. He isaffiliated with the Japanese Physical Therapy Association,
Rehabilitation Engineering Society of Japan, Japanese Society
on Severe Motor and Intellectual Disabilities, and Japan Sport
Association for the Disabled. He specializes in neurologicalphysical therapy, life support and environmental adjustment
physical therapy, and sport training for the disabled.

Yoshinori Mitsuhashi is a professor emeritus and currently a
specially assigned professor of the Faculty of Education andRegional Studies at Fukui University, Japan. He graduatedfrom Kwansei-Gakuin University, Department of Experimental
Psychology in 1972, and finished his graduate study in 1980. He
became an associate professor of Fukui University in 1981 andleft as a professor in 2014. His research covers psychophysiology
of cognition and education of handicapped children. In particular,
he studies the cognitive process in the children and adults withneurodevelopmental disorders using the electrical activity of thebrain.