TOWARDS USING PROSODY IN SPEECH RECOGNITION/UNDERSTANDING 
SYSTEMS: DIFFERENCES BETWEEN READ AND SPONTANEOUS SPEECH 
Kim E.A. Silverman, Eleonora Blaauw l, Judith Spitz, John F Pitrelli 
Speech Technology Group 
Artificial Intelligence Laboratory 
NYNEX Science and Technology 
White Plains, NY, 10604, U.S.A. 
1. Ph.D. student in Institute for Language and Speech, University of Utrecht 
1. ABSTRACT 
A persistent problem for keyword-driven speech recognition sys- 
tems is that users often embed the to-be-recognized words or 
phrases in longer utterances. The recognizer needs to locate the 
relevant sections of the speech signal and ignore extraneous 
words. Prosody might provide an extra source of information to 
help locate target words embedded in other speech. In this paper 
we examine some prosodic characteristics of 160 such utterances 
and compare matched read and spontaneous versions. Half of the 
utterances are from a corpus of spontaneous answers to requests 
for the name of a city, recorded from calls to Directory Assistance 
Operators. The other half are the same word strings read by volun- 
teers attempting to model the real dialogue. Results show a consis- 
tent pattern across both sets of data: embedded city names almost 
always bear nuclear pitch accents and are in their own intonational 
phrases. However the distributions of tonal make-up of these pro- 
sodic features differ markedly in read versus spontaneous speech, 
implying that if algorithms that exploit these prosodic regularities 
are trained on read speech, then the probabilities are likely to be 
incorrect models of real user speech. 
2. INTRODUCTION 
This work addresses two related questions. One is whether 
spontaneous goal-directed utterances collected from real 
users in a particular application domain exhibit reliable pro- 
sodic patterns that could be exploited by recognition algo- 
rithms. We focus on to-be-recognized words that are 
spoken within longer utterances, in order to investigate 
whether these embedded words have particular prosodic 
characteristics that could help a recognizer to locate them. 
One of the original motivations for this study was our 
observation from informal listening to our corpus that such 
embedded words bear nuclear pitch accents. If this is a con- 
sistent pattern, it would mean that they are (1) louder, 
longer and more clearly articulated than they would be 
without nuclear accents, and (2) they would bear character- 
istic fundamental frequency movements. 
Corpora of spontaneous goal-directed speech from real 
users are not readily obtainable, and so it is common prac- 
tice to record speech read out by volunteers in order to 
develop, train and test recognition algorithms. To the extent 
that the prosody of read speech differs from that of sponta- 
neous goal-directed speech, such "laboratory" corpora may 
obscure or misrepresent any reliable prosodic properties 
found in spontaneous "real user" speech. Consequently the 
second question investigated in this work is whether such 
patterns can also be found in recordings of speech reead out 
by volunteers. We are interested in prosodic differences 
between read and spontaneous speech because of their rele- 
vance to speech recognition, and for increasing the natural- 
ness of synthetic speech. 
It is worth pointing out a methodological issue at this stage: 
the prosody used when people are reading can of course 
differ dramatically from that used in spontaneous commu- 
niccation. Speech databases that we know of vary widely in 
how much effort was taken to ensure that the prosody of 
the speech realistically reflects the speech that recognizers 
have to deal with in real-world applications. In this experi- 
ment we chose to do everything we could to encourage our 
volunteers to use realistic spontaneous-sounding prosody. 
Most existing speech corpora used in the recognition field 
have been collected with less emphasis on realistic pros- 
ody. We therefore believe that the read speech in this exper- 
iment is as similar as possible to spontaneously produced 
utterances. The degree of prosodic similarity that we report 
between read and spontaneous speech represent a "best 
case". 
3. DISCOURSE DOMAIN 
Our particular application is automatic recognition of the 
name of a city in telephone calls to a Directory Assistance 
Operator. A corpus of 26,946 recordings of real users has 
been collected and was reported on in detail in a previous 
DARPA meeting\[l\]. In each case a caller was played an 
automated request for the name of a city. In 37% of those 
utterances that do contain a city name, that city name is 
embedded in a longer string of connected speech. In some 
cases there is relatively little extraneous material ("In Bos- 
ton, please"), but often (55% in this corpus) there is con- 
siderably more ("Yes Ma' m Central Auto Service in 
435 
Stoughton, please."). We refer to these as "complex embed- 
ded" utterances. In all of these embedded utterances speak- 
ers have considerably more options concerning how they 
say the speech than when they say only an isolated city 
name, and so we would expect prosody to contribute signif- 
icantly to the variability in the signal. The current study 
deals with the complex embedded utterances, because (i) 
these represent the more serous challenge for speech rec- 
ognition, and (ii) these contain richer prosodic variation 
that is more representative of spontaneous speech in other 
discourse domains. 
Although the current study focuses on a telephone network 
application, we believe that the results have general appli- 
cability to the behavior of users of spoken language systems 
(SLS). Often users will answer a request for a single item of 
information with a sentence containing not only the 
requested item, but also (1) extraneous material, and (2) 
answers to anticipated subsequent requests in the discourse. 
For example, in an ATIS-like domain, a user may answer a 
request for a destination with "I'd like to arrive in Boston 
on Tuesday morning before 9:30 am", bundling the arrival 
day and arrival time into the same utterance. Prosody can 
mark all of the discourse-relevant information-bearing 
words in such an answer, and so could help a SLS to avoid 
reprompting for material that has already been said. 
For this investigation, we selected 80 of the spontaneous 
complex embedded utterances that reflected the variation in 
length and structure of the larger set. Each utterance was 
spoken by a different speaker, half of the speakers were 
male and half were female. The shortest utterance was two 
words ("Arlington, McCarthy") and the longest was twenty 
words ("Have you a listing in Jamaica Plain for Robert 
Scheinkopf - S - C - H - E - I - N - K - 0 - P - F"). Half of 
the 80 utterances were in "telegram" style - bearing few or 
no function words ("Boston Woolworth's on Washington 
Street"), and the set was chosen to reflect variation in 
whether the target city name was a first, medial, or last con- 
tent word in the utterance. 
We then collected a matched corpus in which volunteers 
called an automated recording facility in our laboratory and 
read out orthographic transcriptions of the same utterances. 
Participants knew that these texts were originally spoken by 
people calling Directory Assistance asking for information 
about a telephone number, and were encouraged to rehearse 
the items several times before calling with this in mind. 
Participants confirmed during subsequent debriefing that 
they had tried to make their utterances sound realistically 
natural, acting out the situation of making a telephone call 
to get information. Each participant read a list of 25 sen- 
tences: the first and last two were fillers, and one of the 
middle sentences was the utterance relevant to the current 
study. This "read speech" corpus consists of 2000 utter- 
ances altogether, collected from 80 volunteers, of which 
one utterance per reader is used in this investigation. The 
reader of each utterance was of the same sex as the speaker 
of the spontaneous version. 
4. PROSODIC ANALYSIS METHODS 
We use "prosody" to refer to the acoustic/phonetic bracket- 
ing structure, locations of boundaries, and the choice and 
distribution of tonal features. This suprasegmental organi- 
zation has been shown to affect not only duration and fun- 
damental frequency but also such phenomena as co- 
articulation, devoicing, laryngealization, and allophonic 
variation. Thus it is a potential information source for fac- 
toring out variability in acoustic-phonetic models, locating 
word boundaries, disambiguating alternative parses or inter- 
pretations, and locating embedded keywords. In this study 
we focus primarily on the last of these. 
The prosody in the read and spontaneous versions of the 
utterances was manually transcribed by two people via 
interactive listening and graphical displays of the speech 
waveform, total signal energy, and extracted fundamental 
frequency contour. This signal-processing and display was 
performed with version 2.0 of the WAVES+ software pack- 
age\[4\]. Each transcriber labelled an overlapping subset of 
the utterances, enabling us to compare their transcriptions 
for almost half of the corpus. In addition, a number of direct 
measurements were taken from the acoustic signals. 
4.1. Prosodic Transcription Scheme 
The utterances were transcribed using the draft prosodic 
transcription conventions developed at the First Prosodic 
Transcription Workshop hosted by the Spoken Language 
Systems Group in MIT in August 1991. Briefly, this is a set 
of labels for the tonal (pitch) structure and boundary struc- 
ture of spoken American English. The tonal labels are a 
subset of Pierrehumbert's model\[2\]: this approach views 
pitch contours as composed of a sparse linear sequence of 
accents and tones selected from a relatively small inventory. 
In the draft scheme used below, the inventory of pitch 
accents is reduced to H*, L*, L+H*, L*+H, and the down- 
step feature is marked explicitly (e.g. H* versus :H*). Lack 
of confidence is marked by affixing a "?" after the symbol. 
Boundaries are a subset of the break indices used by Price et 
al \[3\]. The labelling process consisted of locating and iden- 
tifying the pitch accents, phrase accents, and boundary 
tones, and assigning a strength to each inter-word boundary 
(from 0 => cliticized word; to 4 => full intonational phrase 
boundary). A transcriber can affix a + or - to indicate uncer- 
tainty about the strength of a boundary. 
One of the transcribers received one day's training before- 
hand, and supplemented that by reading portions of Pierre- 
humbert \[2\] and Silverman \[5\]. The other transcriber 
received about a half day's training. Both transcribers 
would occasionally consult with the first author concerning 
particularly unclear phenomena. 
436 
5. RESULTS 
5.1. Reliability Across Transcribers 
One of the stated aims of the First Prosodic Transcription 
Workshop was that the transcription conventions should be 
easily taught, and that different transcribers should agree at 
least 80% of the time. 
We could test this in this experiment, because 36 of the 
spontaneous and read versions (i.e. 72 utterances in all) 
were labelled by both transcribers. Because transcriptions 
are linear strings of symbols, one way to calculate agree- 
ment between 2 transcribers is: 
Matches Agreement = I00( ) 
Matches + Insertions + Substitutions 
where: 
Matches = number of symbols in the string where the tran- 
scribers agree concerning location and the symbol itself 1, 
Insertions = number of symbols marked by one transcriber 
only (an omission by either transcriber is equivalent to an 
insertion by the other), and Substitutions = number of loca- 
tions where each transcriber used a different symbol. 
Table 1 shows the agreement separately calculated for the 
tonal and boundary transcriptions under two criteria. Over- 
all the agreement is quite satisfactory. Exact match means 
both transcribers had to use exactly the same symbols in the 
same locations to score a match. Near match slightly 
relaxes the criteria for matching in the following ways: 
Near tonal match: (1) phrase-initial H* matches phrase- 
initial L+H*; (2) a H* or L+H* match the corresponding 
downstepped variants of themselves (!H* and !L+H*, 
respectively), (3) an accent matches its uncertain variant 
(e.g. H* matches H*?) 
Near boundary match: (1) a 0 (= clificized word) matches 
a 1 (= normal phrase-medial interword boundary), (2) a 1 
matches a 2 (= separation between words, but with no tonal 
correlates of a boundary) 
If agreement includes near-matches, then we have clearly 
met the reliability criteria. If not, then we still have met it in 
the tonal transcriptions, but not in the boundary transcrip- 
tions. Most of the disagreements concerned whether some 
word boundaries were cliticized (e.g. between the first and 
second words in "Could I have the..." versus "C' d I have 
the..."). In the subsequent preliminary analyses of the tonal 
1. Final boundaries at the fight-hand edge of utterances 
are excluded from this analysis because they would arti- 
ficially inflate the agreement scores: both transcribers 
agreed 100% that all utterances ended with a 4 bound- 
ary. 
transcriptions, we used the more experienced transcriber's 
decisions in cases where there is disagreement. 
Exact Match Near Match 
Tonal 81% 92% 
Structure 
Boundary 68% 94% 
Structure 
Table 1: Percent agreement between Iranscfibers on 
tonal and boundary structure. 
5.2. Comparison of Read and Spontaneous 
Versions: Intonation 
Our initial informal impression which motivated this study 
was borne out by the transcriptions: in both corpora the 
embedded city names usually bear a nuclear accent (94% of 
spontaneous, 97% of read utterances, no significant differ- 
ences), and are set off in an intonational phrase of their own. 
Moreover the tonal combinations carried by these city 
names represent only a relatively small subset of the possi- 
ble combinations that can occur in spoken English. Within 
the transcription framework used in this study, there are 16 
different possible combinations of pitch accent, phrase 
accent and boundary tone (the three tonal elements of a city 
name in most of our corpus). However, the only five that 
actually occurred on the city names were: 
Pitch accent Phrase accent 
Final 
boundary 
tone a 
H* L L\] 
L+H* L L\] 
L* H H\] 
H* H H\] 
L+H* H H\] 
a. To avoid confusion with (i) the results expressed below as 
percentages, and (ii) initial boundary tones in Pierrehumbert 
\[2\], we follow the convention in Silverman \[5\] of using "\]" 
instead of "%" for final boundary tones. 
The first two of these tunes are falls, the last three are types 
of rises. These same five tunes occurred in both the read and 
the spontaneous corpora. We interpret this as another simi- 
larity between the read and spontaneous corpora: the read- 
ers not only succeeded in putting nuclear accents on the city 
names, but also chose from the same inventory that is used 
in spontaneous interactions in this domain. However 
437 
although the embedded city names were almost all nuclear 
in the read and spontaneous utterances, the distributions of 
the five tunes across the corpora were not at all the same. It 
is commonplace in the literature to categorize nuclear pitch 
very grossly movements into rising (or high level) versus 
falling. This corresponds in our case largely to the phrase 
accent being H or L (this would not be the case if there had 
been any L phrase accents followed by HI boundary tones). 
In Table 2 we compare the read and spontaneous versions 
of 791 of the pairs of city names according to this gross 
division. For the few city names that bore prenuclear H* 
accents, we categorized them as falling if the next accent 
was either a L* or downstepped, else as rising. 
Spontaneous 
version 
Rising 
Falling 
Read version 
Rising Falling 
27% 47% 
8% 19% 
74% 
27% 
Table 2: Agreement between spontaneous and read 
versions of each city name. All percentages are out of the 
79 pairs included in this analysis 
One common view of prosody is that it is determined by 
syntax, that there is a default prosody for any given sen- 
tence which is derivable from the word string itself. If this 
is true, or if the way city names are read out resembles how 
they are spoken spontaneously, then the data should be con- 
centrated in the upper left and lower right cells of Table 2. 
In fact, less than half of the data (46%) lies in these two 
cells. The main reason is that 47% of the city names were 
spoken with a rising intonation in spontaneous versions, but 
with falling intonation in the read versions. 
This shift from rising to falling intonation is also reflected 
in the marginal totals: 73% of the spontaneous city names 
had rises, but only 34% of their read counterparts did. The 
data argue that prosody is not directly derivable from the 
word string itself. Two possible reasons for this difference 
are: 
• A rising intonation is a marker of politeness in this 
particular dialogue context. When volunteers partici- 
pate in a recording session, even when they attempt 
to act out the real dialogue, they do not feel com- 
pelled to be polite to the recording equipment. 
• In the real interaction with a telephone operator the 
speaker uses rising intonation to seek confirmation 
that the operator has indeed understood the city 
1. One of the read versions was truncated in such a way 
that it had to be left out of this analysis 
name. Speakers may not be consciously aware that 
they do this, and so fail to replicate it when attempt- 
ing to emulate the interaction 
The preponderance of falls in read speech, when compared 
to the preponderance of rises in spontaneous speech, has a 
number of implications for speech technology. One of these 
concerns the acoustic models in a recognizer: low final 
boundary tones (which are located at the right hand edge of 
most of the falling nuclear accents, and therefore are com- 
mon in read speech) tend to be associated with laryngealiza- 
tion and devoicing of the segmental material. Consequently 
these spectral effects will be built into acoustic models that 
are trained on read speech, but will be the exception rather 
than the rule in the spontaneous speech that a recognizer 
will ultimately have to process. 
These rising-versus-falling differences also bear on a poten- 
tial use of prosody for speech understanding systems: in a 
system where the user can both ask questions and also 
deliver answers to questions asked by the system itself, the 
natural language processing part of the discourse manager 
could be helped if it could use prosody to distinguish 
between these two different speech acts. Suggestions have 
been made that questions usually have high or rising intona- 
tion, whereas information-delivering statements usually 
have falling intonation. The current results indicate that at 
least in the application domain used in this experiment, this 
distinction is more complicated. Users are delivenng infor- 
mation in response to a question from the system, rather 
than asking the system a direct question themselves, and yet 
they use rising tunes more often than falling. 
The tonal differences between the corpora were not 
restricted to the phrase accents alone. In read speech, 81% 
of the city names carried a H*, whereas in spontaneous 
speech this was only 52%, with the remaining cases bearing 
either a L÷H* (35%) or a L* (8%). The majority of the city 
names were final in their intonational phrase, and therefore 
contained an additional boundary tone on the right. In the 
spontaneous corpus, 76% of these were H\] and 12% were 
L\]. Once again, this order was reversed in the read corpus 
(28% were HI and 72% L\]). 
5.3. Comparison of Read and Spontaneous 
Versions: Pauses 
The characteristics and distribution of pauses in these utter- 
ances also showed reliable patterns and important differ- 
ences between read and spontaneous speech. The following 
summary is based on all pauses in the utterances, not just 
those around city names. Some pauses occurred at "gram- 
matical" positions, as in: 
"In Boston <...> may I have the number of..." 
"...the number John Smith <...> in Boston", 
others at "ungrammatical" positions: 
438 
"yes the number of <...> John Smith in <...> Boston". 
This classification of pause types is common in the litera- 
ture. While it seems to have intuitive appeal, we believe 
that it may be more of a continuum than a clear category 
distinction. Ungrammatical pauses may be reinterpreted as 
merely being located at more embedded levels of bracket- 
ing in a syntactic structure than grammatical pauses. At 
least in some eases the labelling of a pause as grammatical 
or ungrammatical may be a consequence of the researcher's 
preferred syntactic theory. In the current study, 91% of the 
ungrammatical pauses were located after the preposition 
within a prepositional phrase. 
Like O'Shaughnessy \[6\], we found that while some pauses 
are located at grammatical boundaries, others are not. But 
the ratio distinguished between the two speech modes: 45% 
of all pauses were "ungrammatical" in the spontaneous 
speech, but only 11% in the read speech. Unlike O'Shaugh- 
nessy, we found that in both corpora ungrammatical pauses 
were longer than grammatical ones. Silent pauses in gram- 
matical locations were twice as long in spontaneous speech 
(mean 0.45 seconds, standard deviation 0.29) as in read 
speech (mean 0.21 seconds, standard deviation 0.15). In the 
read corpus there was less variability in pause duration 
(mean 0.23 seconds, standard deviation 0.17) than in the 
spontaneous speech (mean 0.45 seconds, standard deviation 
0.29). 85% of the filled pauses were located at ungrammati- 
cal positions One striking difference between the corpora 
was that in the read versions there were no filled pauses at 
all. Moreover, in only 18% of the read utterances did the 
readers place pauses in the same places as they occurred in 
the spontaneous versions. All of these were grammatical 
boundaries ("Cambridge <silence> I'm looking for Pizza 
Ring") which also carried full intonational phrase bound- 
aries. All other differences consisted of either omitting 
ungrammatical pauses or inserting grammatical ones where 
the original speakers did not. 
We believe that in the spontaneous speech the ungrammati- 
cal pauses, and perhaps also some of the grammatical ones, 
reflect the speakers' lexical access delay and mark for the 
listeners that the post-pausal words are not easily predict- 
able (i.e. information-rich) and therefore "worth waiting 
for". In read speech there is no comparable lexical access 
because all the words are already laid out on the printed 
orthography, and consequently this component of the infor- 
mation structure is not marked in the readers' utterances. 
5.4. Prosodic Characteristics of Other Words 
Although we do not yet have quantitative analysis specific 
to non-target speech, we do notice two consistent prosodic 
patterns in the remaining parts of the utterances outside of 
the city names. 
The first pattern is that content words that are not directly 
conveying discourse-relevant information either bear no 
accent at all, or at their most salient bear only pre-nuclear 
accents and are not set off in phrases by themselves. Exam- 
pies include the parenthesized words in: 
"Could I (please) have the (number)for Watertown Police" 
"Cambridge I'm (looking)for Pizza Ring" 
"(I'm) (trying) to (find) the (exchange)for Cape Cod". 
The second pattern returns us to the issue raised earlier in 
this paper that users often anticipate what questions will be 
asked subsequently in the dialogue. In the Directory Assis- 
tance domain subsequent questions will be for the name, 
and if that is likely to be ambiguous then there will be a 
request for further disambiguating information. The consis- 
tent behavior of users in our corpus is to mark this informa- 
tion in a similar way to how they mark the city name. 
Examples include: 
"Quincy the Imperial Gardens on Sea Street" 
"Yes I'd like the number of the Langley Deli in Newton 
please." 
"Uh this is Quincy I'd like the number of the Quincy Police, 
not the emergency number of course." 
One similarity is that these items tend to bear nuclear 
accents. But they differ in that these accents are often 
nuclear in an intermediate phrase, rather than a full intona- 
tional phrase. Thus they do not have the extra boundary 
tone, they exhibit less phrase-final lengthening, and are less 
likely to be followed by pauses. 
Another common prosodic pattern arises when these exlxa 
compound nouns consist of more than one word, as illus- 
trated in the above examples. Typically each word will bear 
a pitch accent, but they will not all be of the same type. The 
first accent is usually a L+H*, whereas subsequent ones are 
simple H*. That causes fundamental frequency to start low 
and rise to the first noun, and then stay high until the last 
one. Thereafter it moves into the phrase accent and is 
accompanied by lengthening of the material. In those cases 
where that phrase accent is L, then this contour appears to 
be an instance from American English of the "hat pattern" 
that has been described for British English and for Dutch 
\[7\]. Often in our spoken corpus the phrase accent is H. But 
in both cases, these patterns combine to somewhat set off 
the whole compound as a separate unit, in a way that could 
be exploited by a recognizer. 
6. CONCLUSION 
Read speech differs from spontaneous speech in some 
important ways: (i) although the tunes on focussed words 
are selected from the same inventory in both read and spon- 
taneeous speech, the prior probabilities of the tunes differ 
greatly -- spontaneous speech predominantly contains rises, 
read speech predominantly contains falls, (ii) pauses in read 
speech are shorter than in spontaneous speech, and they pre- 
439 
dominantly are located at structurally predictable positions 
(grammatical boundaries), whereas in spontaneous speech 
this generalization hardly holds true at all, (iii) read speech 
tends to not contain filled pauses. These differences argue 
that algorithms which are developed to exploit this informa- 
tion will need to be developed and trained on the basis of 
spontaneous speech from real users, rather than just from 
read speech. 
These results are encouraging for locating embedded tar- 
gets in speech recognition tasks: they show that when users 
respond to a query from an automated system, they mark 
the embedded information-bearing words with an acousti- 
cally-salient nuclear pitch accent and often precede and/or 
follow them by a pause. 
For speech synthesis in the context of spoken language sys- 
tems, these results suggest that listeners will better be able 
to understand and interpret synthesized utterances if the 
focussed information that they contain is (i) bears a nuclear 
tune, and (ii) is preceded by some lengthening of the imme- 
diately-preceding material and perhaps even the insertion of 
a short pause. Further investigations will address prediction 
of the tonal makeup of these patterns. 
Acknowledgments 
Sheri Walzman learned prosodic transcription and labored 
long doing careful labelling. Lisa Russell developed the 
automated recording facility, helped find suitable volun- 
teers, and imposed organization and order on the data col- 
lection effort. Without the help of these two people this 
work would never have seen the light of day. Any abuses of 
their work nevertheless remain our own responsibility. 
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