# A fuzzy acoustic-phonetic decoder for speech recognition

03 Oct 1996-Vol. 4, pp 2270-2273

TL;DR: A general framework for acoustic-phonetic modelling is developed and context-sensitive rules are incorporated into a knowledge-based automatic speech recognition (ASR) system and are assessed with control based on fuzzy decision-making.

Abstract: A general framework for acoustic-phonetic modelling is developed. Context-sensitive rules are incorporated into a knowledge-based automatic speech recognition (ASR) system and are assessed with control based on fuzzy decision-making. A reliability measure is outlined, a test collection is run and a confusion matrix is built for each rule. During the recognition procedure, the fuzzy set of trained values related to the phonetic unit to be recognized is computed, and its membership function is automatically drawn. Tests were done on an isolated-word speech database of French with 1000 utterances and 33 rules. The results with a one-speaker low training rate are established via a two-step procedure: word recognition and a word-rejection testbed with five speakers who were never involved during the training.

## Summary (2 min read)

Jump to: [2 . 1 Multi-Stage Decoding] – [2 . 2 Contextual Rules] – [3 . 2 A Reliability Measure.] – [3 . 3 Aggregation] – [4. PERFORMANCE] and [5. CONCLUSIONS]

### 2 . 1 Multi-Stage Decoding

- A bottom-up, rule-based, acoustic-phonetic decoder retrieves the segments and context-free features from isolated words [4].
- Then, a word recognizer [1] provides a set of concurrent lexical hypotheses from the previous phonetic lattice .
- To improve the recognition rate, a top-down decoder is now able to focus on phonetic transitions and to verify coarticulation cues.
- This environment allows the user to program context sensitive rules since all phonetic hypothesis are available during the top-down stage.

### 2 . 2 Contextual Rules

- The system combines three sets of recognition rules which analyse the spectral characteristics of the vocal tract to compute co-articulation features for French.
- The speaker references are obtained with a low training procedure (30 spoken words).
- A set of 24 mel-scaled LPC based cepstrum, energy, zero-crossing and delta zero-crossing rates are computed for each frame.
- The frequency band where burst occurs depends on the right context.
- If the following phoneme is /i/, L starts from the first channel to channel (F2+1) where F2 is extracted from the V-spectral reference.

### 3 . 2 A Reliability Measure.

- Using fuzzy sets initiated by [5], the platform provides a reliability measure in order to gain knowledge about the ability of each rule and to perform rational fusion operators on such degrees of uncertainty.
- Hence, the reliability measure is trained on an isolated-word speech database.
- During the recognition procedure, rule relevance may be computed from such a set of histograms.
- Pj which have been detected into a Pi equivalent signal portion within the lattice.
- L assures a normalization constraint which causes ignorance to get a high uncertainty.

### 3 . 3 Aggregation

- To compute a phonetic score knowing the reliability scores cij (see fusion1 in table 1), the semantic interpretation of cij is used.
- As an average reliability score means either ignorance or high uncertainty, the fusion1 operator solely trusts the lowest and the highest score.
- The experimental weight function w tends to aggregate with the min function if one of the Sj corresponds to a low degree of certainty, otherwise tends to aggregate with the arithmetical mean function.

### 4. PERFORMANCE

- The evaluation speech data were selected from the BDLEX database.
- The reliability measure was poorly trained using a partial database collected from one male speaker.
- The isolatedword recognition corpus consisted of 1000 words preprocessed with a 20,000 word dictionary at bottom-up decoding: a group of five speakers (four males, one female), who were never involved during the learning stage, was presented with 200 words each.
- Thus, the results show the speaker-independent ability of the system.
- 33 rules were applied during the top-down phase.

### 5. CONCLUSIONS

- To summarize, the authors can say that fuzzy decision making has a number of advantages compared with hierarchical control when it comes to reject lexical hypotheses: Thresholds are delayed in the decision procedure; .
- The multi-domain parameters produced by rules can be compared and rationally aggregated after the computation of the reliability measure.
- One is the optimization of aggregation operators.
- On the other hand, the relevance measure has a potential use in other word rejection areas: speech recognition with HMM may improve by evaluating a probability model from reliability vectors, which is currently being investigated in a speaker independent vocal dictation system.

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A FUZZY ACOUSTIC-PHONETIC DECODER

FOR SPEECH RECOGNITION

Olivier OPPIZZI, David FOURNIER, Philippe GILLES, Henri MELONI

CERI - Laboratoire d'informatique, AVIGNON, FRANCE

ABSTRACT

In this paper, a general framework of acoustic-phonetic

modelling is developed. Context sensitive rules are

incorporated into a knowledge-based automatic speech

recognition (ASR) system and are assessed with control based

on fuzzy decision making. The reliability measure is outlined: a

tests collection is run and a confusion matrix is built for each

rule. During the recognition procedure the fuzzy set of trained

values related to the phonetic unit to be recognized is

computed, and its membership function is automatically drawn.

Tests were done on an isolated-word speech database of French

with 1000 utterances and with 33 rules. The results with a one-

speaker low training rate are established via a two-step

procedure: a word recognition and a word rejection test bed with

five speakers who were never involved during the training.

1. INTRODUCTION

Speech Understanding is considered as a dynamic process

through linguistic levels with a high combinatory complexity.

By essence, work in the field of speech recognition tends to

prune research spaces so as to reduce lexical hypothesis to be

further computed by a semantic level. The goal of the described

work is the development of a rule-based isolated-word ASR

system which will give the user a means to incorporate, assess

and apply into a fuzzy framework new rules to be involved in

reducing a lexical cohort.

2. RECOGNITION RULES

2.1 Multi-Stage Decoding

A bottom-up, rule-based, acoustic-phonetic decoder retrieves

the segments and context-free features from isolated words [4].

Then, a word recognizer [1] provides a set of concurrent lexical

hypotheses from the previous phonetic lattice (Figure 1).

Signal (word "peek")

bottom-up APD

+ lexical access

top-down APD

word 1: a

parameters

word 2:

t

ik

p

LATTICE

Figure 1: Principles of a multi-stage decoder.

To improve the recognition rate, a top-down decoder is now

able to focus on phonetic transitions and to verify co-

articulation cues. This environment allows the user to program

context sensitive rules since all phonetic hypothesis are

available during the top-down stage.

2.2 Contextual Rules

The system combines three sets of recognition rules which

analyse the spectral characteristics of the vocal tract to

compute co-articulation features for French. The speaker

references are obtained with a low training procedure

(30 spoken words). The decoder samples input speech at

12800 Hz and divides it into frames every 10ms. A set of 24

mel-scaled LPC based cepstrum, energy, zero-crossing and delta

zero-crossing rates are computed for each frame.

F1 increases if /k,g,r/ appears in context. Let V be

the current spoken vowel hypothesis, L be the frequency band

of F1 references whatever the vowel and S the frequency band

of V-like vowel references (figure 2). S is shifted in a /k/ or /g/

or /r/ context hypothesis so as to open the formant location.

The rule returns (M

S

-M

L

) where M

S

is the spectral maximum of

band S and M

L

the maximum of band L. The returned value is

expected to be positive if V is a vowel in such an opening

context.

i

r (hypothesis)

frame close to /i/ reference frame

min(F1 of /i,y,u/ references)

max(F1 of /i,y,u/ references)

SL

min(F1 of all vowels)

max(F1 of all vowels)

Figure 2: F1 increasing in /k,g,r/ context.

Stop-consonants' burst. The frequency band where burst

occurs depends on the right context. Let V be the stop-

consonant to be analysed and L be the frequency band where

the burst is expected to be found. For instance, if the following

phoneme is /i/, L starts from the first channel to channel

(F2+1) where F2 is extracted from the V-spectral reference. The

rule returns the peak value in L.

High frequency slice of /s,∫/ spectrum. The highest

delta high frequency C is extracted from the fricative reference.

C becomes a band L depending on the right phoneme. For

example, if the right context is /i,e,t,d/, L is [C-1,24]. The

rule returns the higher slice in L.

3. FUZZY DECISION MAKING

3.1 Fuzzy Versus Classical Control.

A classical processing of acoustic-phonetic rules includes

thresholdings anf hierarchical control, as illustrated in

figure 3, to recognize voiceless fricatives.

rule_FF(unit)

f = frame such that (f-1) and (f+1) spectra are the closest

S = spectrum of f

Max = higher value of S in high frequencies

Min = lower value of S in low frequencies

if( |Max-Min| < Threshold1 ) then return /f/ else return ?

rule_SSCH(unit)

S = spectrum of frame f such that (f-1) and (f+1) spectra

are the most distant

F = frequency of S where delta is the highest

if( F < Threshold2 ) then return /∫/ else return /s/

CONTROL: rule_FFSSCH(u)

if( rule_FF(u) ≠ ? ) then return /f/ else return rule_SSCH(u)

Figure 3: Hierarchical decision in a speech recognizer.

A distinctive feature of our system is that the control runs under

a fuzzy model combined to a least-commitment decision

approach. Thus, particular attention has been devoted to the

decision module so that thresholds and hierarchic description of

knowledge are avoided. In table 1, C

Ri

() is a reliability measure

applied to rule R

i

. and c

ij

corresponds to the degree of

certainty to detect phoneme j knowing the result of rule R

i

.

word 'peek': /p/ /i:/ /k/

C

R1

(): c

11

c

12

c

13

C

R2

(): c

21

c

22

c

23

•••

fusion 2 <- (fusion 1 fusion 1 fusion 1)

Table 1: Fuzzy decision in a speech recognizer.

fusion1 and fusion2 are aggregation operators. fusion1

gives a degree of certainty to every phonetic unit of the lattice,

fusion2 computes a lexical score.

To obtain a well-defined decision model, particular attention

has been paid to rules integration: although values returned by

rules can be either of numerical or of symbolic nature (the

reliability measure translates multi-domain values

onto [0,1]), rules have been adjusted to fit the dynamic

ranges of the acoustic cues, whatever the phonetic units to be

decoded. Indeed, no more hierarchical structure can prevent a

rule from analysing of sound with or without such or such a

property, as rule_ffssch() in figure which assumes

rule_ssch() to analyse a non-/f/ phoneme. Hence,

robustness has become a major issue.

3.2 A Reliability Measure.

Using fuzzy sets initiated by [5], the platform provides a

reliability measure in order to gain knowledge about the ability

of each rule and to perform rational fusion operators on such

degrees of uncertainty. This contribution is motivated by

automatic computations of acoustic cue fuzzy descriptions as

stated in [2].

Hence, the reliability measure is trained on an isolated-word

speech database. For every rule and phoneme, a histogram is

established from a confusion matrix. In figure 4, P1 is /i/, P2

is /y/, and R returns the F2-F4 slice. This parameter is expected

to be positive for /i/ and to be negative for /y/.

RULE R

phoneme

P1

occurrences

parameters

(values of R)

v

occurrences

parameters

(values of R)

value v appears n times

in P1 analysis

phoneme

Pn

n

•••

.....

Figure 4: Histograms of rule ability to recognize phonemes.

If R produced negative parameters in the P1 analysis, either

the phoneme shows a bad acoustic quality, or the rule is not

able to analyse correctly the variability of such a sound. Since

the relevance of a rule is related to these two factors, our

reliability measure corresponds to a relevance function.

During the recognition procedure, rule relevance may be

computed from such a set of histograms. Let H

R,Pi

() be the

histogram of rule R for the phonetic hypothesis P

i

to be

analysed. A fuzzy set is represented as a set of parameters (x-

axis in figure ). A fuzzy set and its membership function (the

relevance function) are built using H=H

R,Pi

() as the correct

recognition histogram and H'=H'

R,Pi

() as the wrong

recognition histogram such that:

H

R,P

i

'

: v→ H

R,P

i

'

v

()

= H

R,P

i

v

()

j≠i

∑

The j indices refer to phonemes P

j

which have been detected

into a P

i

equivalent signal portion within the lattice. Actually,

H'

R,Pi

() corresponds to the erroneous recognition histogram

of rule R for phonetic hypothesis P

i

against a set of candidate

phonemes. The way to elaborate the relevance function is

shown in figure 5 (parameters are along the x-axis).

word 1:

f

li

C (/i/)

p

p=R( /i/ )

H histogram of R for /i/

H' histogram of R for /i/

(against a set of

candidate phonemes)

peak detection

+ aggregation

R

Z' peak

Z peak

Z' block Z block

fuzzyfication

in [0,1]

.

.

Z' level

Z level

word 2:

f

a:

(candidate phoneme)

Figure 5: A relevance function as a membership function

built during the recognition procedure.

After peak detection over histograms, the procedure shows

reactive zones where correct and/or erroneous recognition

results have been trained. Levels of the reliability measure are

computed by a sophisticated function L() including

possibilist calculus and training rates. For a given rule:

Let N (resp. N' ) be the cardinality of histogram H (resp. H' )

Let Z be a block (interval of parameters) of H and Z' of H'

LZ

()

=

lZ/H

()

−lZ/H'

()

+1

2

lZ/H

()

=

∆Z

()

sup

z of H

∆ z

()

()

⋅

log

2

N

()

log

2

N

()

+4

⋅

log N

()

log max(N, N')

()

l(Z/H) comes out as the relevance of block Z knowing

histogram H. The first factor expresses the possibility [3] of a

Z block among blocks of H, with ∆(Z] the density of

block Z. The second and third factors decrease the result

respectively if H corresponds to an absolute low trainig rate

and if H corresponds to a low training rate relative to H'.

No training results over a given block Z is called ignorance

(that is, l(Z/H)=l(Z/H')=0). L() assures a normalization

constraint which causes ignorance to get a high uncertainty.

Once levels are computed, they are joined by lines. Thus, rule

domains have to be linear. Linear interpolation seems to be

relevant in fuzzy applications [3] as far as fuzzy

representations tend to capture unprecise data.

In this way, it is not necessary to engage a high training rate

procedure to have a fuzzy description of the reliability measure,

as it is by HMM decoding. Moreover, the reliability measure is

not a global measure. For a given rule, it varies according to the

parameter returned by a given rule and according to any pre-

decoding: in our ASR system, histograms are determined from

the bottom-up decoder ability to discriminate between sounds.

3.3 Aggregation

To compute a phonetic score knowing the reliability scores c

ij

(see fusion1 in table 1), the semantic interpretation of c

ij

is

used. As an average reliability score means either ignorance or

high uncertainty, the fusion1 operator solely trusts the lowest

and the highest score. If the N values of c

ij

are ordered for a

given phoneme j (c

1j

is the lowest and c

Nj

the highest

reliability score), fusion1 can be seen as an OWA

operator [7] with a null weight vector [w

i

]

1≤i≤N

but first and

last weight, expressing that c

1j

and c

Nj

scores are more

weighted as they go far from 0.5.

w

1

= c

1j

− 0.5

w

N

= c

Nj

− 0.5

∀i = 2,...,N −1

(

)

,w

i

= 0

and fusion1 =

w

i

⋅ c

ij

()

i=1

N

∑

w

i

()

i=1

N

∑

To compute fusion2 (Table 1), it is considered that a low

phonetic score coming from fusion1 implies a low lexical

score, that is, a word may be rejected if it is sure that one of its

phonetic hypotheses is not available in the speech signal. A

context independent variable behaviour operator [6] is used as

illustrated in figure 6. Let S

j

(i=1,...,P) be the P phonetic

scores to combine. The experimental weight function w()

tends to aggregate with the min() function if one of the S

j

corresponds to a low degree of certainty, otherwise tends to

aggregate with the arithmetical mean function.

fusion2 = wS

j

()

⋅min S

j

()

[]

+1−wS

j

⋅

S

j

j=1

P

∑

P

w()

min(Sj)

0

0.5

1

1

Figure 6: experimental weight function for aggregation.

4. PERFORMANCE

The evaluation speech data were selected from the BDLEX

database. The reliability measure was poorly trained using a

partial database collected from one male speaker. The isolated-

word recognition corpus consisted of 1000 words pre-

processed with a 20,000 word dictionary at bottom-up

decoding: a group of five speakers (four males, one female),

who were never involved during the learning stage, was

presented with 200 words each. Thus, the results show the

speaker-independent ability of the system. For every word to be

recognized, a 50 candidate word cohort was available. 33 rules

were applied during the top-down phase.

The experiment consisted of testing the recognition and the

rejection ability of the top-down decoder. Figure 7 shows the

correct recognition results in cumulated percentages and

table 2 the rejection rates among 49000 erroneous words and

1000 correct words.

0

20

40

60

80

100

1

7

13

19

25

31

37

43

49

Figure 7: correct recognition results.

threshold

on lexical score

erroneous words

rejection rate

correct words

rejection rate

10 2.31% 0.08%

16 11.3% 3.4%

20 40.6% 29.15%

Table 2: rejection rate according to a relevance threshold.

The top-down decoder fails to significantly improve the

performance of the bottom-up session. Firstly, only

ten phoneme-context dependent rules were tested. We hope the

system can perform better with the addition of such rules.

Secondly, examination of the fuzzy decision model shows that

reliability scores often correspond to a high degree of

uncertainty. Therefore, the decision itself becomes uncertain.

The system was observed to provide interesting but insufficient

rejection rates if the lexical score is below 16. The

aggregation model produces safe decisions as long as most of

the phonemes of a given word were not acoustically depreciated

(34 words are rejected at threshold=16 since two phonetic

scores at least per word were low due to two rules at least).

These results stress the importance of considering acoustic-

phonetic knowledge to reject erroneous lexical hypotheses

rather than obtaining a high recognition rate.

5. CONCLUSIONS

To summarize, we can say that fuzzy decision making has a

number of advantages compared with hierarchical control when

it comes to reject lexical hypotheses:

• Thresholds are delayed in the decision procedure;

• It is not necessary to extract a rule control procedure

from meta-knowledge;

• The multi-domain parameters produced by rules can be

compared and rationally aggregated after the

computation of the reliability measure.

The system presented above can be improved on in a number of

ways. One is the optimization of aggregation operators. On the

other hand, the relevance measure has a potential use in other

word rejection areas: speech recognition with HMM may

improve by evaluating a probability model from reliability

vectors, which is currently being investigated in a speaker

independent vocal dictation system.

6. ACKNOWLEDGMENTS

The authors would like to thank Renato De Mori for helpful

discussions.

7. REFERENCES

1. Béchet, F., Système de traitement de connaissances

phonétiques et lexicales: application à la

reconnaissance de mots isolés sur de grands

vocabulaires et à la recherche de mots cibles dans un

discours continu, PhD Thesis of the University of

Avignon, France, 1994.

2. De Mori, R., Computer Models of Speech using Fuzzy

Algorithms, Plenum Press, New York, 1983.

3. Dubois, D., Modèles mathématiques de l'imprécis et de

l'incertain en vue d'applications aux techniques d'aide à

la décision, PhD Thesis of the Institut National

Polytechnique de Grenoble, France, 1983.

4. Gilles, P, Décodage phonétique de la parole et

adaptation au locuteur, PhD Thesis of the University of

Avignon, France, 1993.

5. Zadeh, L.A., "Fuzzy Sets", Information Control: 338-

353, Vol. 8, 1965.

6. Bloch, I., Information Combination Operators for Data

Fusion: A Comparative Review with Classification,

Technical report n° 94 D 013, Ecole Nationale

Supérieure des Télécommunications, France, 1994

7. Yager, R.R., "On Ordered Weighted Averaging

Aggregation Operators in Multicriteria Decision-

Making", Readings in Fuzzy Sets for Intelligent

Systems: 80-87, 1993

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01 Jan 1996TL;DR: A classification of operators issued from the different data fusion theories with respect to their behavior provides a guide for choosing an operator in a given problem and can be refined from the desired properties of the operators, from their decisiveness, and by examining how they deal with conflictive situations.

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