Formalizing Knowledge Used in Spectrogram Reading: Acoustic

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Formalizing Knowledge Used in Spectrogram Reading:
Acoustic and Perceptual Evidence from Stops
RLE Technical Report No. 537
December 1988
Lori Faith Lamel
Research Laboratory of Electronics
Massachusetts Institute of Technology
Cambridge, MA 02139 USA
This work has been supported by the Defence Advanced Research
Projects Agency, Vinton-Hayes, Bell Laboratories (GRPW), and
Inference Corporation.
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11. TITLE (Include Security Cassificaon)
Formalizing Knowledge Used in Spectrogram Reading: Acoustic and Perceptual Evidence
From Stops.
12. PERSONAL AUTHOR(S)
Lori F. Lamei
13a. TYPE OF REPORT 13b. TIME COVERED 114. DATE OF REPORT (Year, Month, Day) IS. PAGE COUNT
'technical Report FROM TO December 1988 185
16. SUPPLEMENTARY NOTATION
Technical Report 537, Research Laboratory of Electronics, Massachusetts Institute of
Technology, Cam'bridge, MA, 1988.
17. COSATI CODES 8. SUBJECT TERMS (Continue on reverse if necesry and identify by block number)
FIELD GROUP SUB-GROUP
19. ABSTRACT (Continue on reverse if necessary and identify by block number)
Please see next page
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19. ABSTRACT
Since the invention of the sound spectrograph in 1946 by Koenig, Dunn and Lacev,
spectrograms have been widely used for speech research. Over the last decade there has
been revived interest in the application of spectrogram reading toward continuous speech
recognition. Spectrogram reading involves interpreting the acoustic patterns in the image
to determine the spoken utterance. One must selectively attend to many different acoustic
cues, interpret their significance in light of other evidence, and make inferences based on
information from multiple sources. While early attempts at spectrogram reading met
with limited success (Klatt and Stevens, 1973; Lindblom and Svenssen, 1973; Svenssen,
M.
1974), Zue, in a series of experiments intended to illustrate the richness of phonetic
information in the speech signal (Cole et al., 1980; Cole and Zue, 1950), demonstrated
that high performance phonetic labeling of a spectrogram could be obtained.
In this thesis a formal evaluation of spectrogram reading was conducted in order to obtain
a better understanding of the process and to evaluate the ability of spectrogram readers.
I
The research consisted of three main parts: an evaluation of spectrogram readers on
a constrained task, a comparison to listeners on the same task, and a formalization of
spectrogram-reading knowledge in a rule-based system.
The performance of 5 spectrogram readers was assessed using speech from 299 talkers.
The readers identified stop consonants which were extracted from continuous speech and
presented in the immediate phonemic context. The task was designed so that lexical
and other higher sources of knowledge could not be used. The averaged identification
rate of the ranged across contexts, from 73-82% top choice, and 77-93% for the top two
choices. The performance of spectrogram readers was, on the average, 10% below that
of human listeners on the same task. Listeners had an overall identification rate that
L
ranged from 85 to 97%. The performance of readers is comparable to other spectrogram
reading experiments reported in the literature, however the other studies iave typically
evaluated a single subject on speech spoken by a small number of talkers.
Although researchers have suggested that the process can be described in terms of rules
(Zue. 1981), few compilations of rules or strategies exist (Rothenberg, 1963; Fant, 1968,
Svenssen, 1974). In order to formalize the information used in spectrogram reading, a
system for identifying stop consonants was developed. A knowledge-based system was
chosen because the expression and use of the knowledge is explicit. The emphasis was
on capturing the acoustic descriptions and modeling the reasoning thought to be used
by human spectrogram readers. However, the implemention was much harder than had
been anticipated due to a variety of reasons. The most important is that there appears
to be much more happening in our visual system and in our thought processes than we
actually express, even when asked to explain our reasoning. Human are able to selectively
pay attention to acoustic evidence, even in the presence of contradictory evidence. This
ability is not well understood, and is difficult to mimic. The performance of the system
In
was adequate: identification of 94 tokens that were both heard and read correctly was
88% top choice, and 96% top 2.
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11. TITLE (Include Security Clasfication)
Formalizing Knowledge Used in Spectrogram Reading: Acoustic & Perceptual Evidence
From Stops
12. PERSONAL AUTHOR(S)
Lori F. Lamel
13a. TYPE OF REPORT 13b. TIME COVERED t14. DATE OF REPORT (Year, Month, Day) 1S. PAGE COUNT
Technical Report FROM TO _ December 1988 185
16. SUPPLEMENTARY NOTATION
Technical Report 537, Research Laboratory of Electronics, Massachusetts Institute of
Technology, Cambridge, MA, 1988.
17. COSATI COOES 18. SUBJECT TERMS (Continue on reverse if necessary and identify by block number)
Il FIELD GROUP SUB-GROUP
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19, ABSTRACT (Continue on reverse if necessary and identify b bock number)
Please see next page
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19. ABSTRACT
Since the invention of the sound spectrograph in 1946 by Koenig, Dunn and Lacev.
spectrograms have been widely used for speech research. Over the last decade there has
been revived interest in the application of spectrogram reading toward continuous speech _
recognition. Spectrogram reading involves interpreting the acoustic patterns in the image
to determine the spoken utterance. One must selectively attend to many different acoustic
cues. interpret their significance in light of other evidence, and make inferences based on J
information from multiple sources. While early attempts at spectrogram reading met
with limited success (Klatt and Stevens, 1973; Lindblom and Svenssen, 1973; Svenssen,
1974), Zue, in a series of experiments intended to illustrate the richness of phonetic
information in the speech signal (Cole et al., 1980; Cole and Zue, 1980), demonstrated
that high performance phonetic labeling of a spectrogram could be obtained.
In this thesis a formal evaluation of spectrogram reading was conducted in order to obtain
a better understanding of the process and to evaluate the ability of spectrogram readers.
The research consisted of three main parts: an evaluation of spectrogram readers on J
a constrained task. a comparison to listeners on the same task, and a formalization of
spectrogram-reading knowledge in a rule-based system.
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The readers identified stop consonants which were extracted from continuous speech and
presented in the immediate phonemic context. The task was designed so that lexical
and other higher sources of knowledge could not be used. The averaged identification
rate of the ranged across contexts, from 73-82% top choice. and 77-93% for the top two
choices. The performance of spectrogram readers was, on the average, 10% below that
of human listeners on the same task. Listeners had an overall identification rate that
ranged from 85 to 97%. The performance of readers is comparable to other spectrogram
J
reading experiments reported in the literature, however the other studies have typically
evaluated a single subject on speech spoken by a small number of talkers.
Although researchers have suggested that the process can be described in terms of rules
(Zue, 1981), few compilations of rules or strategies exist (Rothenberg, 1963; Fant, 1968.
Svenssen, 1974). In order to formalize the information used in spectrogram reading, a
system for identifying stop consonants was developed. A knowledge-based system was
chosen because the expression and use of the knowledge is explicit. The emphasis was
on capturing the acoustic descriptions and modeling the reasoning thought to be used
by human spectrogram readers. However, the implemention was much harder than had l
been anticipated due to a variety of reasons. The most important is that there appears
to be much more happening in our visual system and in our thought processes than we
actually express, even when asked to explain our reasoning. Human are able to selectively |
pay attention to acoustic evidence, even in the presence of contradictory evidence. This
ability is not well understood, and is difficult to mimic. The performance of the system
was adequate: identification of 94 tokens that were both heard and read correctly was
88% top choice, and 96% top 2.
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11. TITLE (Include Security Cassification)
Formalizing KNowledge Used in Spectrogram Reading: Acoustic and Perceptual Evidence
*11 From Stops
12. PERSONAL AUTHOR(S)
Lori F. Lamel
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Technical Report FROM TO_____ December 1988 185
16. SUPPLEMENTARY NOTATION
Technical Report 537, Research Laboratory of Electronics, Massachusetts Institute of
Technology, Cambridge, MA, 1988.
17. COSATI CODES 18. SUBJECT TERMS (Continue on reverse if necessary and identify by block number)
FIELD GROUP SUB-GROUP
. =~~
r- 19. ABSTRACT (C_o nt.i.nu_e on. re_v erse if necessaryi and. .i.d eni tif.y ib .i y b..loii_ck number)
Please see next page
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_DO Form 1473, JUN 86 Previous editionsa re obsolete. SECURITY CLASSIFICATION OF THIS PAGE
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19. ABSTRACT
Since the invention of the sound spectrograph in 1946 by Koenig, Dunn and Lacev.
spectrograms have been widely used for speech research. Over the last decade there has
been revived interest in the application of spectrogram reading toward continuous speech
recognition. Spectrogram reading involves interpreting the acoustic patterns in the image
to determine the spoken utterance. One must selectively attend to many different acoustic
cues, interpret their significance in light of other evidence, and make inferences based on
information from multiple sources. While early attempts at spectrogram reading met
with limited success (Klatt and Stevens, 1973; Lindblom and Svenssen, 1973; Svenssen,
1974), Zue. in a series of experiments intended to illustrate the richness of phonetic
information in the speech signal (Cole et al., 1980; Cole and Zue, 1980), demonstrated
that high performance phonetic labeling of a spectrogram could be obtained.
In this thesis a formal evaluation of spectrogram reading was conducted in order to obtain
a better understanding of the process and to evaluate the ability of spectrogram readers.
The research consisted of three main parts: an evaluation of spectrogram readers on
a constrained task, a comparison to listeners on the same task, and a formalization of
spectrogram-reading knowledge in a rule-based system.
The performance of 5 spectrogram readers was assessed using speech from 299 talkers.
The readers identified stop consonants which were extracted.from continuous speech and
presented in the immediate phonemic context. The task was designed so that lexiEal
and other higher sources of knowledge could not be used. The averaged identification
rate of the ranged across contexts, from 73-82% top choice, and 77-93% for the top two
choices. The performance of spectrogram readers was, on the average, 10% below that
of human listeners on the same task. Listeners had an overall identification rate that
ranged from 85 to 97%. The performance of readers is comparable to other spectrogram
reading experiments reported in the literature, however the other studies have typically
evaluated a single subject on speech spoken by a small number of talkers.
Although researchers have suggested that the process can be described in terms of rules
(Zue. 1981), few compilations of rules or strategies exist (Rothenberg, 1963; Fant, 1968,
Svenssen. 1974). In order to formalize the information used in spectrogram reading, a
system for identifying stop consonants was developed. A knowledge-based system was
chosen because the expression and use of the knowledge is explicit. The emphasis was
on capturing the acoustic descriptions and modeling the reasoning thought to be used
by human spectrogram readers. However, the implemention was much harder than had
been anticipated due to a variety of reasons. The most important is that there appears
to be much more happening in our visual system and in our thought processes than we
actually express, even when asked to explain our reasoning. Human are able to selectively
pay attention to acoustic evidence, even in the presence of contradictory evidence. This
ability is not well understood, and is difficult to mimic. The performance of the system
was adequate: identification of 94 tokens that were both heard and read correctly was
88% top choice, and 96% top 2.
_ ,_I
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i11. TITLE (Include Security Clasification)
Formalizing Knowledge Used in Spectrogram Reading: Acoustic and Perceptual Evidence
From Stons
12. PERSONAL AUTHOR(S)
Lnri F. Tamel
13a. TYPE OF REPORT 113b. TIME COVERED 14. DATE OF REPORT (Yar,M onth, Day) 1S. PAGE COUNT
Technical Report FROM TO_____ December 1988 185
16. SUPPLEMENTARY NOTATION
Technical Repqrt 537, Research Laboratory of Electronics, Massachusetts Institute of
Technology. Cambridge, MA 1988.-
17. , COSATI CODES 18. SUBJECT TERMS (Continue on revere if necessary and identify by block number)
FIELD [ GROUP SUB-GROUP
19, ABSTRACT (Continue on reverse if necessary and identify by block number)
Please see next page
20. DISTRIBUTION/AVAILABILITY OF ABSTRACT 21. ABSTRACT SECURITY CLASSIFICATION
IlUNCLASSIFIED/UNLIMITED O SAME AS RPT. [ DTIC USERS UNCLASSIFIED
22a. NAME OF RESPONSIBLE INDIVIDUAL 22b. TELEPHONE (Include Area Code) 22c. OFFICE SYMBOL
Elisabeth Colford - RLE Contract Reports [phone]
-DO Form 1473, JUN 86 Previous editions are obsolet. SECURITY CLASSIFICATION OF THIS PAGE
UNCLASSIFIED
SICUITY CLASSIFICATION OF THIS PAGE
19. ABSTRACT
Since the invention of the sound spectrograph in 1946 by Koenig, Dunn and Lacey,
spectrograms have been widely used for speech research. Over the last decade there has J
been revived interest in the application of spectrogram reading toward continuous speech
recognition. Spectrogram reading involves interpreting the acoustic patterns in the image J
to determine the spoken utterance. One must selectively attend to many different acoustic
cues, interpret their significance in light of other evidence, and make inferences based on
information from multiple sources. While early attempts at spectrogram reading met J
with limited success (Klatt and Stevens, 1973; Lindblom and Svenssen, 1973; Svenssen,
1974), Zue, in a series of experiments intended to illustrate the richness of phonetic
information in the speech signal (Cole et al., 1980; Cole and Zue, 1980), demonstrated J
that high performance phonetic labeling of a spectrogram could be obtained.
In this thesis a formal evaluation of spectrogram reading was conducted in order to obtain I
a better understanding of the process and to evaluate the ability of spectrogram readers.
Tle research consisted of three main parts: an evaluation of spectrogram readers on
a constrained task, a comparison to listeners on the same task, and a formalization of
spectrogram-reading knowledge in a rule-based system.
The performance of 5 spectrogram readers was assessed using speech from 299 talkers.
The readers identified stop consonants which were extracted from continuous speech and 1
presented in the immediate phonemic context. The task was designed so that lexical
and other higher sources of knowledge could not be used. The averaged identification
rate of the ranged across contexts, from 73-82% top choice, and 77-93% for the top two |
choices. The performance of spectrogram readers was, on the average, 10% below that
of human listeners on the same task. Listeners had an overall identification rate that
ranged from 85 to 97%. The performance of readers is comparable to other spectrogram L
reading experiments reported in the literature, however the other studies have typically
evaluated a single subject on speech spoken by a small number of talkers.
L
Although researchers have suggested that the process can be described in terms of rules
(Zue, 1981), few compilations of rules or strategies exist (Rothenberg, 1963; Fant, 1968,
Svenssen, 1974). In order to formalize the information used in spectrogram reading, a
I
system for identifying stop consonants was developed. A knowledge-based system was
chosen because the expression and use of the knowledge is explicit. The emphasis was
on capturing the acoustic descriptions and modeling the reasoning thought to be used
by human spectrogram readers. However, the implemention was much harder than had 6-
been anticipated due to a variety of reasons. The most important is that there appears
to be much more happening in our visual system and in our thought processes than we
actually express, even when asked to explain our reasoning. Human are able to selectively
pay attention to acoustic evidence, even in the presence of contradictory evidence. This
ability is not well understood, and is difficult to mimic. The performance of the system
was adequate: identification of 94 tokens that were both heard and read correctly was
88% top choice, and 96% top 2.
I-
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~--iiiIIII
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S- -. - Ze -S : - e ae --