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# Recognition of vowels by cochlear implants using a fuzzy logic

Stéphane Gallégo, Emmanuel Perrin, Christian Berger-Vachon, Lionel Collet, Eric Truy

► To cite this version:

Stéphane Gallégo, Emmanuel Perrin, Christian Berger-Vachon, Lionel Collet, Eric Truy. Recognition of vowels by cochlear implants using a fuzzy logic. International IMSEE Symposium Fuzzy Systems and Neural Networks, pp.103-116, 1994. hal-04407804

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# Recognition of vowels by cochlear implants using a fuzzy logic

Stéphane Gallégo, Emmanuel Perrin, Christian Berger-Vachon, Lionel Collet, Eric Truy

► To cite this version:

Stéphane Gallégo, Emmanuel Perrin, Christian Berger-Vachon, Lionel Collet, Eric Truy. Recognition of vowels by cochlear implants using a fuzzy logic. International IMSEE Symposium Fuzzy Systems and Neural Networks, pp.103-116, 1994. hal-04407804

HAL Id: hal-04407804

https://hal.science/hal-04407804v1

Submitted on 3 Mar 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

Public Domain
# RECOGNITION OF VOWELS BY COCHLEAR IMPLANTS
USING A FUZZY LOGIC

S.Gallego, E.Perrin, C.Berger Vachon, L.Collet, E.Truy

Hearing & Voice laboratory (URA-CNRS 1447)
ORL Clinic.
E.Herriot Hospital
69437 LYON CEDEX 03 (France)

## ABSTRACT

In this work the authors have studied the application of a fuzzy logic for the modelling of human behaviour. The subject is the recognition of phonemes by patients wearing a cochlear implant. The acoustic signal is coded by a cochlear implant and acquired on a working station and fed into the hard disk of a desktop computer. Parameters are then extracted from the signal to perform an automatic recognition of French vowels.

Four vowels, embedded in short sentences, have been taken in order to construct two 48-item lists. First list was used for learning and second for recognition. Ten speakers collaborated in this experiment leading to about a thousand utterances.

The lists were played to patients fitted with a cochlear implant. For each patient and for each speaker a confusion matrix was established.

With parameters taken out of the electrical signal delivered by the implant, 255 recognition models were tested.

Two automatic recognitions, using the learning and the recognition lists, were done with each model and the corresponding confusion matrices were calculated and compared with the matrix given by the patient.

Recognitions were based on a fuzzy logic and on an Euclidian distance.

Results showed that fuzzy logic led to results more close to the patients' decision than Euclidian distance.

Then, useful parameters appear to depend strongly upon the patient and this must be considered for the setting of the electrodes.

## I/INTRODUCTION

Processing of acoustical signal by cochlear implant patients is an important issue mostly when the coding performed by the machine is considered [2,11,12].
# RECOGNITION OF VOWELS BY COCHLEAR IMPLANTS
USING A FUZZY LOGIC

S.Gallego, E.Perrin, C.Berger Vachon, L.Collet, E.Truy

Hearing & Voice laboratory (URA-CNRS 1447)
ORL Clinic.
E.Herriot Hospital
69437 LYON CEDEX 03 (France)

## ABSTRACT

In this work the authors have studied the application of a fuzzy logic for the modelling of human behaviour. The subject is the recognition of phonemes by patients wearing a cochlear implant. The acoustic signal is coded by a cochlear implant and acquired on a working station and fed into the hard disk of a desktop computer. Parameters are then extracted from the signal to perform an automatic recognition of French vowels.

Four vowels, embedded in short sentences, have been taken in order to construct two 48-item lists. First list was used for learning and second for recognition. Ten speakers collaborated in this experiment leading to about a thousand utterances.

The lists were played to patients fitted with a cochlear implant. For each patient and for each speaker a confusion matrix was established.

With parameters taken out of the electrical signal delivered by the implant, 255 recognition models were tested.

Two automatic recognitions, using the learning and the recognition lists, were done with each model and the corresponding confusion matrices were calculated and compared with the matrix given by the patient.

Recognitions were based on a fuzzy logic and on an Euclidian distance.

Results showed that fuzzy logic led to results more close to the patients' decision than Euclidian distance.

Then, useful parameters appear to depend strongly upon the patient and this must be considered for the setting of the electrodes.

## I/INTRODUCTION

Processing of acoustical signal by cochlear implant patients is an important issue mostly when the coding performed by the machine is considered [2,11,12].
Several assumptions must be made, ranging from phonetic considerations at the acoustical stage to message understanding at a higher level.

In this work, results obtained with a fuzzy logic are studied compared to performances given by patients wearing a cochlear implant. They are discussed regarding the corresponding features calculated with a more classical distance, an Euclidian metric in this case.

## 1.1: Normal hearing

Ear is the first stage of the auditory mechanism. It can be seen in three parts:

- Outer ear receives acoustic vibrations of air and conveys them to ear-drum.

- Middle ear performs a transformation of acoustical energy into mechanical energy, using ear-drum and ossicles.

- Inner ear is mostly constituted by the cochlea which performs a transformation of mechanical displacements into nervous influx which will be further processed by the brain to take out intensity, tone and frequency of vibrations.

<figure><img src="image_2.png" /><figcaption>figure 1: steps of hearing mechanism</figcaption></figure>

## 1.2: Cochlear implant

The aim of a cochlear implant (CI) is to replace a non functioning cochlea [1,4]. CI provides a direct stimulation of the auditory nerve. A biphasic pulse is delivered on the ear at the ends of the auditory nerve bearing a tonotopic information.
Several assumptions must be made, ranging from phonetic considerations at the acoustical stage to message understanding at a higher level.

In this work, results obtained with a fuzzy logic are studied compared to performances given by patients wearing a cochlear implant. They are discussed regarding the corresponding features calculated with a more classical distance, an Euclidian metric in this case.

## 1.1: Normal hearing

Ear is the first stage of the auditory mechanism. It can be seen in three parts:

- Outer ear receives acoustic vibrations of air and conveys them to ear-drum.

- Middle ear performs a transformation of acoustical energy into mechanical energy, using ear-drum and ossicles.

- Inner ear is mostly constituted by the cochlea which performs a transformation of mechanical displacements into nervous influx which will be further processed by the brain to take out intensity, tone and frequency of vibrations.

<figure><img src="image_1.png" /><figcaption>figure 1: steps of hearing mechanism</figcaption></figure>

## 1.2: Cochlear implant

The aim of a cochlear implant (CI) is to replace a non functioning cochlea [1,4]. CI provides a direct stimulation of the auditory nerve. A biphasic pulse is delivered on the ear at the ends of the auditory nerve bearing a tonotopic information.
<figure><img src="image_3.png" /><figcaption>Figure 2: Block diagram of a cochlear implant</figcaption></figure>

CI is mainly made of two parts :

- An 'external part' where processing of the signal is made.

- An 'implanted part' distributing pulses on the nerve ends.

Principle of the CI used in this work (Nucleus System) is based on the classical properties of the voice [3]. As vowels only are taken, it is classically admitted that they can be represented by their first formants. Formants of the voice are the frequencies with highest energy.

In the cochlea duct, a 22-electrode array is inserted. Each electrode faces ends of the cochlear nerve. In order to simulate different frequencies the electrodes are stimulated according to the formants detected in the acoustic signal. As the automatic detection of formants is still an open question, only maximums situated in two frequency ranges are taken, giving the number of the electrode to stimulate.

<figure><img src="image_4.png" /><figcaption>Figure 3: Structure of a stimulating pulse</figcaption></figure>
<figure><img src="image_2.png" /><figcaption>**Figure 2: Block diagram of a cochlear implant**</figcaption></figure>

CI is mainly made of two parts :

- An 'external part' where processing of the signal is made.

-An 'implanted part' distributing pulses on the nerve ends.

Principle of the CI used in this work (Nucleus System) is based on the classical properties of the voice [3]. As vowels only are taken, it is classically admitted that they can be represented by their first formants. Formants of the voice are the frequencies with highest energy.

In the cochlea duct, a 22-electrode array is inserted. Each electrode faces ends of the cochlear nerve. In order to simulate different frequencies the electrodes are stimulated according to the formants detected in the acoustic signal. As the automatic detection of formants is still an open question, only maximums situated in two frequency ranges are taken, giving the number of the electrode to stimulate.

<figure><img src="image_3.png" /><figcaption>**Figure 3: Structure of a stimulating pulse**</figcaption></figure>
Each stimulation is a biphasic pulse. The surface (length X amplitude) of the pulse represents the energy attached to the formant. Stimulation rate is the voice pitch(fundamental frequency). This working is basically true when vowels are uttered; in other circumstances, coding is more complex and needs to be studied more thoroughly.

Four parameters can be taken out of each pulse:

-Number of the stimulated electrode (F).

-Amplitude (A) and duration (T) of the pulse.

-Surface (AxT), called energy (E) of the pulse.

As each formant is represented by two pulses (P1 and P2), eight parameters were used to characterise a vowel.

### 1.3: Research on electrical parameters

Direct analysis of electrical parameters has already been done by several research groups, mostly in Europe.

Dillier (University of Zurich) [5] compared different coding strategies (PES, CIS, CA, IP, ...) and the influence of the frequency range until 8 kHz. It turned out that the use of a 8 kHz bandwidth led to better results than a 4 kHz bandwidth.

Doering (Medical Faculty, Aachen, Germany) [6] showed that there is a slight difference according to the signal processing strategy used, regarding LPC (linear predictive coding) coding or the Nucleus principle for instance. This was done for vowels /φ/, /a/ and /o/ in German.

G.Collet and C.Berger Vachon (E.Herriot Hospital, Lyon) suggested that formants position is not the only cue used by patients for performing the distinction between vowels. This work was done with the Nucleus machine taken in the WSP (wearable speech processor) mode which uses the pitch and the first two-formants.

The present study is a continuation of this work with a larger set of parameters leading to 255 models according to the combinatory theory.

M.Maillot and C.Berger Vachon [10] established that classical analyses of speech, using a formant detection done by ILS (interactive laboratory system) software, did not match perfectly the recognition performed with the maximums detected by the Nucleus system.
Each stimulation is a biphasic pulse. The surface (length X amplitude) of the pulse represents the energy attached to the formant. Stimulation rate is the voice pitch(fundamental frequency). This working is basically true when vowels are uttered; in other circumstances, coding is more complex and needs to be studied more thoroughly.

Four parameters can be taken out of each pulse:

-Number of the stimulated electrode (F).

-Amplitude (A) and duration (T) of the pulse.

-Surface (AxT), called energy (E) of the pulse.

As each formant is represented by two pulses (P1 and P2), eight parameters were used to characterise a vowel.

### 1.3: Research on electrical parameters

Direct analysis of electrical parameters has already been done by several research groups, mostly in Europe.

Dillier (University of Zurich) [5] compared different coding strategies (PES, CIS, CA, IP, ...) and the influence of the frequency range until 8 kHz. It turned out that the use of a 8 kHz bandwidth led to better results than a 4 kHz bandwidth.

Doering (Medical Faculty, Aachen, Germany) [6] showed that there is a slight difference according to the signal processing strategy used, regarding LPC (linear predictive coding) coding or the Nucleus principle for instance. This was done for vowels /φ/, /a/ and /o/ in German.

G.Collet and C.Berger Vachon (E.Herriot Hospital, Lyon) suggested that formants position is not the only cue used by patients for performing the distinction between vowels. This work was done with the Nucleus machine taken in the WSP (wearable speech processor) mode which uses the pitch and the first two-formants.

The present study is a continuation of this work with a larger set of parameters leading to 255 models according to the combinatory theory.

M.Maillot and C.Berger Vachon [10] established that classical analyses of speech, using a formant detection done by ILS (interactive laboratory system) software, did not match perfectly the recognition performed with the maximums detected by the Nucleus system.
# II/ MATERIAL AND METHODS:

## 2.1: Acquisition of the signal

The acoustic signal undergoes several stages before it is processed according to automatic recognition methods.

<figure><img src="image_5.png" /><figcaption>Figure 4: Block diagram of the acquisition system.</figcaption></figure>

Acoustical material had been recorded on a Revox tape recorder. It was then played at the input of a cochlear implant (Nucleus WSP III), which delivers pulses to an acquisition card. Parameters of the pulses were then detected in the signal and fed into a 386 IBM Desktop computer before being stored on hard and floppy disks attached to the system.

The core of the system, the acquisition card, works with a Texas TMS-25 which is controlled by specialised routines written in assembler language. The surrounding programs, written in Turbo Pascal, control the data flow from implant to storage volumes.

The eight parameters taken for the vowels are :

F1 F2 A1 A2 T1 T2 E1 E2.

where:

F indicates the electrode number attached to a formant,

A is the amplitude of the pulse,

T is the duration of the pulse,

E is the energy.

1 and 2 give the number of the formants.

It is not necessary to take the eight parameters to perform a recognition ; according to the phonetic theory, only F1 and F2 should be important for a distinction between vowels. Delattre's triangle (figure 5) shows this situation.

In order to establish the importance of the parameters, all the possible combinations of the eight parameters were studied, leading to 255 possibilities. Obviously, the '00000000' combination meaning that no parameter is taken has been excluded.
## II/ MATERIAL AND METHODS:

### 2.1: Acquisition of the signal

The acoustic signal undergoes several stages before it is processed according to automatic recognition methods.

<figure><img src="image_4.png" /><figcaption>Figure 4: Block diagram of the acquisition system.</figcaption></figure>

Acoustical material had been recorded on a Revox tape recorder. It was then played at the input of a cochlear implant (Nucleus WSP III), which delivers pulses to an acquisition card. Parameters of the pulses were then detected in the signal and fed into a 386 IBM Desktop computer before being stored on hard and floppy disks attached to the system.

The core of the system, the acquisition card, works with a Texas TMS-25 which is controlled by specialised routines written in assembler language. The surrounding programs, written in Turbo Pascal, control the data flow from implant to storage volumes.

The eight parameters taken for the vowels are :

F1 F2 A1 A2 T1 T2 E1 E2.

where:

F indicates the electrode number attached to a formant,

A is the amplitude of the pulse,

T is the duration of the pulse,

E is the energy.

1 and 2 give the number of the formants.

It is not necessary to take the eight parameters to perform a recognition ; according to the phonetic theory, only F1 and F2 should be important for a distinction between vowels. Delattre's triangle (figure 5) shows this situation.

In order to establish the importance of the parameters, all the possible combinations of the eight parameters were studied, leading to 255 possibilities. Obviously, the '00000000' combination meaning that no parameter is taken has been excluded.
<figure><img src="image_6.png" /><figcaption>**Figure 5:** Vowels represented in the Delattre's triangle</figcaption></figure>

## 2.2: Acoustical material

Four oral vowels were considered in this work.

They are /i/, /u/, /a/ and /ɔ/ written in phonetic notation.

Each vowel was randomly repeated 12 times in two 48-item lists.

Each vowel was embedded in a sentence:

'c' est /v/ ça'

('it is /v/ that')

where v stands for the vowel.

First list was the training list and second was the recognition list.

Ten speakers (five males, five females) took part in this experiment, and uttered, each one, two lists involving 980 vowels.

Four patients collaborated with this work; each 980-vowel set was analysed by the CI of each patient with its own setting. Altogether 4,000 vowels with their own parameters were recorded on the mass storage volumes.

## 2.3: Recognition with the patient

Each patient was asked to perform a distinction of the vowels, for each speaker. He had to establish ten confusion matrices. He first listened to the training list, in order to adapt his understanding possibilities. Then, for each utterance, he had to give his best choice for the four vowels chosen for the experiment.

Out of his responses, a confusion matrix was automatically established by the computer. Ten confusion matrices were calculated for each patient (one for each speaker).
<figure><img src="image_5.png" /><figcaption>Figure 5: Vowels represented in the Delattre's triangle</figcaption></figure>

## 2.2: Acoustical material

Four oral vowels were considered in this work.

They are /i/, /u/, /a/ and /ɔ/ written in phonetic notation.

Each vowel was randomly repeated 12 times in two 48-item lists.

Each vowel was embedded in a sentence:

'c' est /v/ ça'

('it is /v/ that')

where v stands for the vowel.

First list was the training list and second was the recognition list.

Ten speakers (five males, five females) took part in this experiment, and uttered, each one, two lists involving 980 vowels.

Four patients collaborated with this work; each 980-vowel set was analysed by the CI of each patient with its own setting. Altogether 4,000 vowels with their own parameters were recorded on the mass storage volumes.

## 2.3: Recognition with the patient

Each patient was asked to perform a distinction of the vowels, for each speaker. He had to establish ten confusion matrices. He first listened to the training list, in order to adapt his understanding possibilities. Then, for each utterance, he had to give his best choice for the four vowels chosen for the experiment.

Out of his responses, a confusion matrix was automatically established by the computer. Ten confusion matrices were calculated for each patient (one for each speaker).
## 2.4: Automatic recognition

Two strategies were studied, the fuzzy logic [8,9] and the Euclidian distance [7].

### 2.4.1: Fuzzy logic

Let us suppose that k parameters are taken in the model.

For each parameter a table was constructed with the training list, giving for each vowel and each value of the parameter, the number of times the value was observed.

The parameters range was divided into 32 same length intervals, so that, a number between 1 and 32 was possible for each vowel.

At the decision stage, a value $x_i$ (i=1,...,k) was observed for the ith parameter.

The system took the following decision :

- a score was attached to each vowel; it was read on the $x^{th}$ line of the table.

- all the scores were summed up for each vowel, on the k parameters.

- the utterance was then classified to the vowel with the highest score.

A confusion matrix was then constructed with this recognition.

As 255 combinations of the k parameters can be considered, 255 confusion matrices were established for each speaker, and for each patient, leading to more than 10,000 matrices.

Table I shows the establishment of the vowels scores for a parameter.

<table><caption>Table I: Scores of the vowels for a parameter; x was observed.</caption><thead><tr><th>Classes<br>Value<br>for 1 parameter</th><th>/a/</th><th>/i/</th><th>/u/</th><th>/e/</th></tr></thead><tbody><tr><th>1</th><td>1a</td><td>1i</td><td>1u</td><td>1e</td></tr><tr><th>X</th><td>Xa</td><td>Xi</td><td>Xu</td><td>Xe</td></tr><tr><th>32</th><td>32a</td><td>32i</td><td>32u</td><td>32e</td></tr></tbody></table>
## 2.4: Automatic recognition

Two strategies were studied, the fuzzy logic [8,9] and the Euclidian distance [7].

### 2.4.1: Fuzzy logic

Let us suppose that k parameters are taken in the model.

For each parameter a table was constructed with the training list, giving for each vowel and each value of the parameter, the number of times the value was observed.

The parameters range was divided into 32 same length intervals, so that, a number between 1 and 32 was possible for each vowel.

At the decision stage, a value $x_i$ (i=1,...,k) was observed for the ith parameter.

The system took the following decision :

- a score was attached to each vowel; it was read on the $x^{th}$ line of the table.

- all the scores were summed up for each vowel, on the k parameters.

- the utterance was then classified to the vowel with the highest score.

A confusion matrix was then constructed with this recognition.

As 255 combinations of the k parameters can be considered, 255 confusion matrices were established for each speaker, and for each patient, leading to more than 10,000 matrices.

Table I shows the establishment of the vowels scores for a parameter.

<table><caption>Table I: Scores of the vowels for a parameter; x was observed.</caption><thead><tr><th>Classes<br>Value<br>for 1 parameter</th><th>/a/</th><th>/i/</th><th>/u/</th><th>/e/</th></tr></thead><tbody><tr><th>1</th><td>1a</td><td>1i</td><td>1u</td><td>1e</td></tr><tr><th>X</th><td>Xa</td><td>Xi</td><td>Xu</td><td>Xe</td></tr><tr><th>32</th><td>32a</td><td>32i</td><td>32u</td><td>32e</td></tr></tbody></table>
### 2.4.2 Euclidian distance:

Let us suppose, once more, that k parameters are taken on the model. The recognition will be considered in a k-dimensional space.

For each vowel and for each parameter a value ranging from 1 to 32 is observed. Thus, each utterance leads to a point in this k-dimensional space.

With the learning list, 4 centres of gravity were established for the vowels.

An utterance in the recognition list gives a vector x to be classified; this vector will be classified to the nearest centre of gravity, according to the Euclidian formula:

$$D^2(x, v) = \sum_{i=1}^{n} (x_i - v_i)^2$$

where:

x is the sample to be classified,

v is the vowel (class),

i is the i-th dimension (i-th parameter),

k is the dimension of the space,

and D(x,v) is the distance of utterance x to class v.

x is classified to the vowel giving the shortest distance. Again, more than 10,000 matrices were constructed with the strategy.

Figure 6 shows this decision process.

<figure><img src="image_7.png" /><figcaption>Figure 6: classification according to the Euclidian distance</figcaption></figure>
### 2.4.2 Euclidian distance:

Let us suppose, once more, that k parameters are taken on the model. The recognition will be considered in a k-dimensional space.

For each vowel and for each parameter a value ranging from 1 to 32 is observed. Thus, each utterance leads to a point in this k-dimensional space.

With the learning list, 4 centres of gravity were established for the vowels.

An utterance in the recognition list gives a vector x to be classified; this vector will be classified to the nearest centre of gravity, according to the Euclidian formula:

$$D^2(x, v) = \sum_{i=1}^{n} (x_i - v_i)^2$$

where:

x is the sample to be classified,

v is the vowel (class),

i is the i-th dimension (i-th parameter),

k is the dimension of the space,

and D(x,v) is the distance of utterance x to class v.

x is classified to the vowel giving the shortest distance. Again, more than 10,000 matrices were constructed with the strategy.

Figure 6 shows this decision process.

<figure><img src="image_6.png" /><figcaption>Figure 6: classification according to the Euclidian distance</figcaption></figure>
## 2.5: Evaluation of the importance of the parameters

As the aim of the work is to simulate the patients' recognition, a Hamming distance was calculated between the matrices established with the models and the patients' performances.

The following formula was used:

$$H(M, P) = \sum_{i=1}^{4} \sum_{j=1}^{4} |m_{ij} - p_{ij}|$$

where:

M is for the model and P for the patient.

mij is the recognition in cell(i,j) of the confusion matrix given by the model, and pij is the corresponding value obtained with the patient.

The absolute value of the difference was taken.

For each comparison, the following indications must be given:

-description of the model.

-name of the patient.

-name of the speaker.

-protocol (fuzzy or Euclidian).

An example of this calculation is given in figure 7

Mode F2F1, patient CO, speaker Ga.

Computer.(Model)

<table><tr><td>CAL</td><td>A</td><td>I</td><td>U</td><td>E</td></tr><tr><td>A</td><td>6</td><td>0</td><td>0</td><td>6</td></tr><tr><td>I</td><td>0</td><td>12</td><td>0</td><td>0</td></tr><tr><td>U</td><td>0</td><td>0</td><td>11</td><td>1</td></tr><tr><td>E</td><td>0</td><td>0</td><td>0</td><td>12</td></tr></table>

Patient

<table><tr><td>PAT</td><td>A</td><td>I</td><td>U</td><td>E</td></tr><tr><td>A</td><td>5</td><td>0</td><td>4</td><td>3</td></tr><tr><td>I</td><td>0</td><td>10</td><td>1</td><td>1</td></tr><tr><td>U</td><td>0</td><td>0</td><td>12</td><td>0</td></tr><tr><td>E</td><td>1</td><td>0</td><td>1</td><td>10</td></tr></table>

$$ \text{Error=Hamming distance: } 37.5 $$

<caption>Figure 7: calculation of the Hamming distance between two confusion matrices.</caption>

# III/ RESULTS AND DISCUSSION

## 3.1: Influence of the fuzzy logic

Distances between matrices have been calculated (table II) using Euclidian distance and fuzzy logic.

Results are given for each patient and averaged over all the speakers.
## 2.5: Evaluation of the importance of the parameters

As the aim of the work is to simulate the patients' recognition, a Hamming distance was calculated between the matrices established with the models and the patients' performances.

The following formula was used:

$$H(M, P) = \sum_{i=1}^{4} \sum_{j=1}^{4} |m_{ij} - p_{ij}|$$

where:

M is for the model and P for the patient.

mij is the recognition in cell(i,j) of the confusion matrix given by the model, and pij is the corresponding value obtained with the patient.

The absolute value of the difference was taken.

For each comparison, the following indications must be given:

- description of the model.

- name of the patient.

- name of the speaker.

- protocol (fuzzy or Euclidian).

An example of this calculation is given in figure 7

Mode F2F1, patient CO, speaker Ga.
Computer.(Model)

<table><thead><tr><th>CAL</th><th>A</th><th>I</th><th>U</th><th>E</th></tr></thead><tbody><tr><th>A</th><td>6</td><td>0</td><td>0</td><td>6</td></tr><tr><th>I</th><td>0</td><td>12</td><td>0</td><td>0</td></tr><tr><th>U</th><td>0</td><td>0</td><td>11</td><td>1</td></tr><tr><th>E</th><td>0</td><td>0</td><td>0</td><td>12</td></tr></tbody></table>

Patient

<table><thead><tr><th>PAT</th><th>A</th><th>I</th><th>U</th><th>E</th></tr></thead><tbody><tr><th>A</th><td>5</td><td>0</td><td>4</td><td>3</td></tr><tr><th>I</th><td>0</td><td>10</td><td>1</td><td>1</td></tr><tr><th>U</th><td>0</td><td>0</td><td>12</td><td>0</td></tr><tr><th>E</th><td>1</td><td>0</td><td>1</td><td>10</td></tr></tbody></table>

Error=Hamming distance: 37.5

<caption>Figure 7: calculation of the Hamming distance between two confusion matrices.</caption>

# III/ RESULTS AND DISCUSSION

## 3.1: Influence of the fuzzy logic

Distances between matrices have been calculated (table II) using Euclidian distance and fuzzy logic.

Results are given for each patient and averaged over all the speakers.
It can be seen that patients' recognition is closer to fuzzy results than those obtained with Euclidian distance.

Average of Hamming distance is 85 with the fuzzy logic and 105 with Euclidian distance; results are always better when fuzzy representation is used.

This trend is seen again (table III) on the overall results. Difference seem to be bigger with females' voices than with males' ones, but this has to be proved to be significant. Also, F2 turned out to be the best parameter, but the energy of the formants cannot be ignored, mostly when fuzzy logic is considered.

<table><caption>1 first parameter 2 second parameter 3 third parameter</caption><thead><tr><th>Patient</th><th>Method</th><th>F2</th><th>A2</th><th>E2</th><th>T2</th><th>F1</th><th>A1</th><th>E1</th><th>T1</th><th>Σ</th></tr></thead><tbody><tr><td>AM</td><td>Euclidian</td><td>95</td><td>109</td><td>106</td><td>97</td><td>102</td><td>125</td><td>106</td><td>100</td><td>105</td></tr><tr><td>BA</td><td>Euclidian</td><td>116</td><td>116</td><td>113</td><td>113</td><td>114</td><td>120</td><td>114</td><td>113</td><td>115</td></tr><tr><td>CO</td><td>Euclidian</td><td>76</td><td>100</td><td>92</td><td>100</td><td>92</td><td>113</td><td>96</td><td>100</td><td>96</td></tr><tr><td>LA</td><td>Euclidian</td><td>105</td><td>108</td><td>105</td><td>102</td><td>105</td><td>115</td><td>105</td><td>102</td><td>106</td></tr></tbody></table>

<table><caption>Table II: Mean distances between models and patients' recognition for both fuzzy and Euclidian methods</caption><thead><tr><th>Patient</th><th>Method</th><th>F2</th><th>A2</th><th>E2</th><th>T2</th><th>F1</th><th>A1</th><th>E1</th><th>T1</th><th>Σ</th></tr></thead><tbody><tr><td>AM</td><td>Fuzzy</td><td>79</td><td>83</td><td>78</td><td>79</td><td>76</td><td>82</td><td>78</td><td>79</td><td>80</td></tr><tr><td>BA</td><td>Fuzzy</td><td>94</td><td>94</td><td>91</td><td>93</td><td>98</td><td>94</td><td>91</td><td>93</td><td>94</td></tr><tr><td>CO</td><td>Fuzzy</td><td>76</td><td>81</td><td>82</td><td>84</td><td>77</td><td>78</td><td>81</td><td>84</td><td>80</td></tr><tr><td>LA</td><td>Fuzzy</td><td>90</td><td>88</td><td>87</td><td>85</td><td>87</td><td>88</td><td>87</td><td>84</td><td>87</td></tr></tbody></table>

<table><caption>1 first parameter 2 second parameter 3 third parameter</caption><thead><tr><th>Speakers</th><th>Method</th><th>F2</th><th>A2</th><th>E2</th><th>T2</th><th>F1</th><th>A1</th><th>E1</th><th>T1</th><th>Σ</th></tr></thead><tbody><tr><td rowspan="2">Female</td><td>Euclidian</td><td>95</td><td>107</td><td>101</td><td>100</td><td>100</td><td>116</td><td>103</td><td>101</td><td>103</td></tr><tr><td>Fuzzy</td><td>80</td><td>84</td><td>80</td><td>81</td><td>79</td><td>83</td><td>80</td><td>80</td><td>81</td></tr><tr><td rowspan="2">Male</td><td>Euclidian</td><td>101</td><td>110</td><td>107</td><td>106</td><td>106</td><td>121</td><td>108</td><td>107</td><td>108</td></tr><tr><td>Fuzzy</td><td>89</td><td>90</td><td>89</td><td>90</td><td>90</td><td>89</td><td>89</td><td>90</td><td>90</td></tr><tr><td rowspan="2">Total</td><td>Euclidian</td><td>98</td><td>108</td><td>104</td><td>103</td><td>103</td><td>118</td><td>105</td><td>104</td><td>106</td></tr><tr><td>Fuzzy</td><td>85</td><td>87</td><td>84</td><td>85</td><td>85</td><td>86</td><td>84</td><td>85</td><td>85</td></tr></tbody></table>

<caption>Table III: Overall comparisons between methods and patients' recognition</caption>
It can be seen that patients' recognition is closer to fuzzy results than those obtained with Euclidian distance.

Average of Hamming distance is 85 with the fuzzy logic and 105 with Euclidian distance; results are always better when fuzzy representation is used.

This trend is seen again (table III) on the overall results. Difference seem to be bigger with females' voices than with males' ones, but this has to be proved to be significant. Also, F2 turned out to be the best parameter, but the energy of the formants cannot be ignored, mostly when fuzzy logic is considered.

<table><caption>1 first parameter 2 second parameter 3 third parameter</caption><thead><tr><th>Patient</th><th>Method</th><th>F2</th><th>A2</th><th>E2</th><th>T2</th><th>F1</th><th>A1</th><th>E1</th><th>T1</th><th>Σ</th></tr></thead><tbody><tr><td>AM</td><td>Euclidian</td><td>95</td><td>109</td><td>106</td><td>97</td><td>102</td><td>125</td><td>106</td><td>100</td><td>105</td></tr><tr><td>BA</td><td>Euclidian</td><td>116</td><td>116</td><td>113</td><td>113</td><td>114</td><td>120</td><td>114</td><td>113</td><td>115</td></tr><tr><td>CO</td><td>Euclidian</td><td>76</td><td>100</td><td>92</td><td>100</td><td>92</td><td>113</td><td>96</td><td>100</td><td>96</td></tr><tr><td>LA</td><td>Euclidian</td><td>105</td><td>108</td><td>105</td><td>102</td><td>105</td><td>115</td><td>105</td><td>102</td><td>106</td></tr></tbody></table>

<table><caption>Table II: Mean distances between models and patients' recognition for both fuzzy and Euclidian methods</caption><thead><tr><th>Patient</th><th>Method</th><th>F2</th><th>A2</th><th>E2</th><th>T2</th><th>F1</th><th>A1</th><th>E1</th><th>T1</th><th>Σ</th></tr></thead><tbody><tr><td>AM</td><td>Fuzzy</td><td>79</td><td>83</td><td>78</td><td>79</td><td>76</td><td>82</td><td>78</td><td>79</td><td>80</td></tr><tr><td>BA</td><td>Fuzzy</td><td>94</td><td>94</td><td>91</td><td>93</td><td>98</td><td>94</td><td>91</td><td>93</td><td>94</td></tr><tr><td>CO</td><td>Fuzzy</td><td>76</td><td>81</td><td>82</td><td>84</td><td>77</td><td>78</td><td>81</td><td>84</td><td>80</td></tr><tr><td>LA</td><td>Fuzzy</td><td>90</td><td>88</td><td>87</td><td>85</td><td>87</td><td>88</td><td>87</td><td>84</td><td>87</td></tr></tbody></table>

<table><caption>1 first parameter 2 second parameter 3 third parameter</caption><thead><tr><th>Speakers</th><th>Method</th><th>F2</th><th>A2</th><th>E2</th><th>T2</th><th>F1</th><th>A1</th><th>E1</th><th>T1</th><th>Σ</th></tr></thead><tbody><tr><td rowspan="2">Female</td><td>Euclidian</td><td>95</td><td>107</td><td>101</td><td>100</td><td>100</td><td>116</td><td>103</td><td>101</td><td>103</td></tr><tr><td>Fuzzy</td><td>80</td><td>84</td><td>80</td><td>81</td><td>79</td><td>83</td><td>80</td><td>80</td><td>81</td></tr><tr><td rowspan="2">Male</td><td>Euclidian</td><td>101</td><td>110</td><td>107</td><td>106</td><td>106</td><td>121</td><td>108</td><td>107</td><td>108</td></tr><tr><td>Fuzzy</td><td>89</td><td>90</td><td>89</td><td>90</td><td>90</td><td>89</td><td>89</td><td>90</td><td>90</td></tr><tr><td rowspan="2">Total</td><td>Euclidian</td><td>98</td><td>108</td><td>104</td><td>103</td><td>103</td><td>118</td><td>105</td><td>104</td><td>106</td></tr><tr><td>Fuzzy</td><td>85</td><td>87</td><td>84</td><td>85</td><td>85</td><td>86</td><td>84</td><td>85</td><td>85</td></tr></tbody></table>

<caption>Table III: Overall comparisons between methods and patients' recognition</caption>
## 3.2: Parameters influence.

Best parameters were calculated, single, pairs and triplets, for Euclidian and fuzzy methods. (table IV)

Sets differ with the patient, and the energy factor cannot be ignored, mostly for the first formant which is predominant. This fact emphasizes the importance of the setting (not only the threshold) of the electrodes.

Difference between patients should be more deeply studied, mostly with the consideration of patients' history and settings of electrodes.

Furthermore, if a transformation of the signal is made by a computer, before delivering pulses to the patient, results given by the theory must be carefully considered prior doing any work that can be done with the patients.

Classical results such as :

-best single : F2.

-best pair : F1F2.

-best triplet : F1F2 (A1 or A2)

were observed with patient CO who is a star patient (very good use of CI) in our clinic.

### Six best parameters:

<table><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>T2</td><td>F2</td><td>T1</td><td>F1</td><td>A2</td><td>A1</td></tr><tr><td>BA</td><td>T1</td><td>T2</td><td>A2</td><td>F1</td><td>F2</td><td>A1</td></tr><tr><td>CO</td><td>F2</td><td>A2</td><td>F1</td><td>A1</td><td>T1</td><td>T2</td></tr><tr><td>LA</td><td>T2</td><td>F1</td><td>T1</td><td>A1</td><td>A2</td><td>F2</td></tr></tbody></table>

### Six best pairs:

<table><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>T2T1</td><td>T2F1</td><td>F2T1</td><td>F2T2</td><td>F1F2</td><td>A2T2</td></tr><tr><td>BA</td><td>A2T1</td><td>A2T2</td><td>A2A1</td><td>A1T1</td><td>T2A1</td><td>T1T2</td></tr><tr><td>CO</td><td>F2F1</td><td>F2A1</td><td>F2A2</td><td>F2T1</td><td>F2T2</td><td>F1A1</td></tr><tr><td>LA</td><td>A2T2</td><td>T2A1</td><td>A1T1</td><td>T2T1</td><td>A2T1</td><td>F1A1</td></tr></tbody></table>

### Six best triplets:

<table><caption>Table IV(a)</caption><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>T2A1T1</td><td>A2T2T1</td><td>T2F1T1</td><td>A2T2F1</td><td>T2F1A1</td><td>F2T2T1</td></tr><tr><td>BA</td><td>A2A1T1</td><td>T2A1T1</td><td>A2T2T1</td><td>A2T2A1</td><td>A2F1T1</td><td>A2F1A1</td></tr><tr><td>CO</td><td>F2A2F1</td><td>F2F1A1</td><td>F2A2A1</td><td>F2T2F1</td><td>F2E2F1</td><td>F2F1T1</td></tr><tr><td>LA</td><td>A2T2A1</td><td>F1A1T1</td><td>A2T2F1</td><td>T2F1A1</td><td>A2F1T1</td><td>T2A1T1</td></tr></tbody></table>
## 3.2: Parameters influence.

Best parameters were calculated, single, pairs and triplets, for Euclidian and fuzzy methods. (table IV)

Sets differ with the patient, and the energy factor cannot be ignored, mostly for the first formant which is predominant. This fact emphasizes the importance of the setting (not only the threshold) of the electrodes.

Difference between patients should be more deeply studied, mostly with the consideration of patients' history and settings of electrodes.

Furthermore, if a transformation of the signal is made by a computer, before delivering pulses to the patient, results given by the theory must be carefully considered prior doing any work that can be done with the patients.

Classical results such as :

-best single : F2.

-best pair : F1F2.

-best triplet : F1F2 (A1 or A2)

were observed with patient CO who is a star patient (very good use of CI) in our clinic.

### Six best parameters:

<table><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>T2</td><td>F2</td><td>T1</td><td>F1</td><td>A2</td><td>A1</td></tr><tr><td>BA</td><td>T1</td><td>T2</td><td>A2</td><td>F1</td><td>F2</td><td>A1</td></tr><tr><td>CO</td><td>F2</td><td>A2</td><td>F1</td><td>A1</td><td>T1</td><td>T2</td></tr><tr><td>LA</td><td>T2</td><td>F1</td><td>T1</td><td>A1</td><td>A2</td><td>F2</td></tr></tbody></table>

### Six best pairs:

<table><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>T2T1</td><td>T2F1</td><td>F2T1</td><td>F2T2</td><td>F1F2</td><td>A2T2</td></tr><tr><td>BA</td><td>A2T1</td><td>A2T2</td><td>A2A1</td><td>A1T1</td><td>T2A1</td><td>T1T2</td></tr><tr><td>CO</td><td>F2F1</td><td>F2A1</td><td>F2A2</td><td>F2T1</td><td>F2T2</td><td>F1A1</td></tr><tr><td>LA</td><td>A2T2</td><td>T2A1</td><td>A1T1</td><td>T2T1</td><td>A2T1</td><td>F1A1</td></tr></tbody></table>

### Six best triplets:

<table><caption>Table IV(a)</caption><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>T2A1T1</td><td>A2T2T1</td><td>T2F1T1</td><td>A2T2F1</td><td>T2F1A1</td><td>F2T2T1</td></tr><tr><td>BA</td><td>A2A1T1</td><td>T2A1T1</td><td>A2T2T1</td><td>A2T2A1</td><td>A2F1T1</td><td>A2F1A1</td></tr><tr><td>CO</td><td>F2A2F1</td><td>F2F1A1</td><td>F2A2A1</td><td>F2T2F1</td><td>F2E2F1</td><td>F2F1T1</td></tr><tr><td>LA</td><td>A2T2A1</td><td>F1A1T1</td><td>A2T2F1</td><td>T2F1A1</td><td>A2F1T1</td><td>T2A1T1</td></tr></tbody></table>
<table><caption>Six best parameters:</caption><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>E1</td><td>T1</td><td>E2</td><td>F2</td><td>F1</td><td>T2</td></tr><tr><td>BA</td><td>E1</td><td>E2</td><td>T1</td><td>T2</td><td>F2</td><td>A2</td></tr><tr><td>CO</td><td>F2</td><td>A2</td><td>E1</td><td>A1</td><td>E2</td><td>F1</td></tr><tr><td>LA</td><td>T2</td><td>T1</td><td>E2</td><td>E1</td><td>F2</td><td>A1</td></tr></tbody></table>

<table><caption>Six best pairs:</caption><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>F1E1</td><td>E2F1</td><td>E1T1</td><td>E2T1</td><td>F2F1</td><td>T2F1</td></tr><tr><td>BA</td><td>E1T1</td><td>A1E1</td><td>E2A1</td><td>A2E2</td><td>E2T1</td><td>T2E1</td></tr><tr><td>CO</td><td>F2F1</td><td>F2A1</td><td>F2E1</td><td>F2A2</td><td>A2A1</td><td>A1E1</td></tr><tr><td>LA</td><td>E1T1</td><td>A2T2</td><td>E2T2</td><td>A2T1</td><td>E2T1</td><td>E2F1</td></tr></tbody></table>

<table><caption>Six best triplets:</caption><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>F2F1E1</td><td>F2E2F1</td><td>E2T2F1</td><td>A2E2F1</td><td>T2F1E1</td><td>E2F1A1</td></tr><tr><td>BA</td><td>A2E2T2</td><td>T2E1T1</td><td>A2T2E1</td><td>A2E2E1</td><td>F2A2E1</td><td>A2T2T1</td></tr><tr><td>CO</td><td>F2F1A1</td><td>F2A2F1</td><td>F2E2F1</td><td>F2F1E1</td><td>F2A1E1</td><td>F2E1T1</td></tr><tr><td>LA</td><td>A2T2T1</td><td>E2T2T1</td><td>T2E1T1</td><td>A2E1T1</td><td>A2T2E1</td><td>A2E2T1</td></tr></tbody></table>

<caption>Table IV(b)</caption>

<caption>Table VI : Best sets of parameters to approximate the patients recognition
(a : Euclidian, b : Fuzzy)</caption>

IV/CONCLUSION

Fuzzy logic introduced an interesting means to approximate human performances, compared to classical Euclidian distances. Further work will be done in order to improve this first step.

Second formant turned out to be the best parameter able to perform the distinction between studied vowels. This was mostly seen with a star patient and when Euclidian distance is taken.

Amplitude of the formants proved to be an important factor for the recognition.

Classical coding (F1F2) turned out to be the best pair only for the star patient. Generally speaking, it can be said that each patient has its own strategy. Analysis of this point will be an important issue of the research in the field of cochlear implant.
<table><caption>Six best parameters:</caption><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>E1</td><td>T1</td><td>E2</td><td>F2</td><td>F1</td><td>T2</td></tr><tr><td>BA</td><td>E1</td><td>E2</td><td>T1</td><td>T2</td><td>F2</td><td>A2</td></tr><tr><td>CO</td><td>F2</td><td>A2</td><td>E1</td><td>A1</td><td>E2</td><td>F1</td></tr><tr><td>LA</td><td>T2</td><td>T1</td><td>E2</td><td>E1</td><td>F2</td><td>A1</td></tr></tbody></table>

<table><caption>Six best pairs:</caption><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>F1E1</td><td>E2F1</td><td>E1T1</td><td>E2T1</td><td>F2F1</td><td>T2F1</td></tr><tr><td>BA</td><td>E1T1</td><td>A1E1</td><td>E2A1</td><td>A2E2</td><td>E2T1</td><td>T2E1</td></tr><tr><td>CO</td><td>F2F1</td><td>F2A1</td><td>F2E1</td><td>F2A2</td><td>A2A1</td><td>A1E1</td></tr><tr><td>LA</td><td>E1T1</td><td>A2T2</td><td>E2T2</td><td>A2T1</td><td>E2T1</td><td>E2F1</td></tr></tbody></table>

<table><caption>Six best triplets:</caption><thead><tr><th>Patient</th><th>1st</th><th>2nd</th><th>3rd</th><th>4th</th><th>5th</th><th>6th</th></tr></thead><tbody><tr><td>AM</td><td>F2F1E1</td><td>F2E2F1</td><td>E2T2F1</td><td>A2E2F1</td><td>T2F1E1</td><td>E2F1A1</td></tr><tr><td>BA</td><td>A2E2T2</td><td>T2E1T1</td><td>A2T2E1</td><td>A2E2E1</td><td>F2A2E1</td><td>A2T2T1</td></tr><tr><td>CO</td><td>F2F1A1</td><td>F2A2F1</td><td>F2E2F1</td><td>F2F1E1</td><td>F2A1E1</td><td>F2E1T1</td></tr><tr><td>LA</td><td>A2T2T1</td><td>E2T2T1</td><td>T2E1T1</td><td>A2E1T1</td><td>A2T2E1</td><td>A2E2T1</td></tr></tbody></table>

<caption>Table IV(b)</caption>

<caption>Table VI : Best sets of parameters to approximate the patients recognition
(a : Euclidian, b : Fuzzy)</caption>

IV/CONCLUSION

Fuzzy logic introduced an interesting means to approximate human performances, compared to classical Euclidian distances. Further work will be done in order to improve this first step.

Second formant turned out to be the best parameter able to perform the distinction between studied vowels. This was mostly seen with a star patient and when Euclidian distance is taken.

Amplitude of the formants proved to be an important factor for the recognition.

Classical coding (F1F2) turned out to be the best pair only for the star patient. Generally speaking, it can be said that each patient has its own strategy. Analysis of this point will be an important issue of the research in the field of cochlear implant.
**ACKNOWLEDGEMENTS:**

Authors wish to acknowledge the supports of institutions and people who back them in their research: the Civil Hospitals of Lyon, the Rhône-Alpes Region, the French Council for Research (CNRS), API company, the University of Lyon, and Professor A.Morgon from the Edouard Herriot hospital.

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A High Spectral Transmission coding strategy for multi-electrode cochlear implant.

Advances in cochlear implant, Edited Hochmair (University of Innsbruck Austria). (1994): 152-157.

[6] Doering (W.H.), Schneider (L)
Electrical vs. acoustical speech patterns of German phonemes using the Nucleus CI-system.

Cochlear Implant. Acquisition and controversies. Edited Bernard Fraysse (Toulouse) (1989): 243-253.
**ACKNOWLEDGEMENTS:**

Authors wish to acknowledge the supports of institutions and people who back them in their research: the Civil Hospitals of Lyon, the Rhône-Alpes Region, the French Council for Research (CNRS), API company, the University of Lyon, and Professor A.Morgon from the Edouard Herriot hospital.

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Cochlear Implant. Acquisition and controversies. Edited Bernard Fraysse (Toulouse) (1989): 243-253.
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[10] Maillot (M), Berger Vachon (C), Truy (E), Morgon (A).
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[11] Wilson (B.S), Finley (C.C), Farmer (J.C), Lawson (D.T) :
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[12] Wilson (B.S), Lawson (D.T), Wolford (R), Eddington (D.K) :
Coding strategies for multichannel cochlear prostheses.
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[7] Diday (E), Lemaire (J), Poujet (J), Testu (F)
Eléments d'analyse de données.
Ed. Dunod (1982).

[8] Kaufman (A).
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Ed. Masson (1975).

[9] Kaufman (A).
Les logiques Humaines et Artificielles.
Ed. Hermes (1988).

[10] Maillot (M), Berger Vachon (C), Truy (E), Morgon (A).
Overlap of Vowel Clusters Through a Nucleus Cochlear Implant.
Advances in cochlear implant, Edited Hochmair (University of Innsbruck
Austria). (1994) : 260-265.

[11] Wilson (B.S), Finley (C.C), Farmer (J.C), Lawson (D.T) :
Comparative studies of speech processing strategies for cochlear implants.
Laryngoscope (1988) 98 : 1039-1077.

[12] Wilson (B.S), Lawson (D.T), Wolford (R), Eddington (D.K) :
Coding strategies for multichannel cochlear prostheses.
Am.J.of Otol. (1991) 12(Suppl) : 56-61.