NTIA Technical Report TR-09-459 

Relationships Between Intelligibility, 

Speaker Identification, and the Detection 

of Dramatized Urgency

Andrew A. Catellier 

Stephen D. Voran 

NTIA Technical Report TR-09-459 

Relationships Between Intelligibility, 

Speaker Identification, and the Detection 

of Dramatized Urgency 

Andrew A. Catellier 

Stephen D. Voran 

U.S. DEPARTMENT OF COMMERCE 

Carlos M. Gutierrez, Secretary 

Meredith A. Baker, Acting Assistant Secretary 

for Communications and Information 

November 2008 

DISCLAIMER 

Certain commercial equipment and materials are identified in this report to specify adequately 

the technical aspects of the reported results. In no case does such identification imply 

recommendations or endorsement by the National Telecommunications and Information 

Administration, nor does it imply that the material or equipment identified is the best available 

for this purpose. 

iii 

CONTENTS 

Page 

FIGURES....................................................................................................................................... vi 

TABLES ........................................................................................................................................ vi 

ABBREVIATIONS/ACRONYMS............................................................................................... vii 

1 INTRODUCTION ........................................................................................................................1 

2 PREVIOUS RESEARCH .............................................................................................................3 

3 SPEECH RECORDINGS .............................................................................................................4 

3.1 Intelligibility ...............................................................................................................4 

3.2 Speaker Identification .................................................................................................4 

3.3 Detection of Speaker “Stress”.....................................................................................5 

4 SPEECH PROCESSING CONDITIONS.....................................................................................8 

5 EXPERIMENT DETAILS..........................................................................................................10 

5.1 Speech Intelligibility.................................................................................................10 

5.2 Speaker Identification ...............................................................................................11 

5.3 Detection of Dramatized Urgency ............................................................................16 

6 RESULTS ...................................................................................................................................18 

6.1 Speech Intelligibility.................................................................................................18 

6.2 Speaker Identification ...............................................................................................20 

6.3 Detection of Dramatized Urgency ............................................................................26 

7 COMBINED RESULTS AND DISCUSSION...........................................................................28 

8 ACKNOWLEDGEMENTS........................................................................................................31 

9 REFERENCES ...........................................................................................................................32 

APPENDIX: PHOTO ATTRIBUTIONS ......................................................................................35 

v 
FIGURES 

Page 

Figure 1. Pitch histograms for four cases as labeled....................................................................... 6

Figure 2. Speaker identification training interface for listeners. .................................................. 12

Figure 3. Speaker identification quiz interface for listeners—initial state. .................................. 13

Figure 4. Speaker identification quiz interface for listeners—after listener selection.................. 14

Figure 5. Speaker identification quiz interface for listeners—correct answer display................. 15

Figure 6. Intelligibility experiment: NTP values for three cases—solid line for single play 

experiment, dashed line for the first play, and dotted line for the last play in the 

multiplay experiment. .................................................................................................... 19

Figure 7. Intelligibility experiment: NTP mean and 95% CI for word intelligibility in sentence 

context............................................................................................................................ 20

Figure 8. SID experiment: Mean fraction correctly identified by listener and 95% CI for each 

listener............................................................................................................................ 21

Figure 9. SID experiment: Fraction correctly identified and 95% CI by speaker and condition. 24

Figure 10. SID experiment: NTP mean and 95% CI by condition. .............................................. 25

Figure 11. Dramatized Urgency Experiment: NTP mean and 95% CI for detection of dramatized 

urgency........................................................................................................................... 27

Figure 12. NTP mean and 95% CI for word intelligibility in a sentence context (green), detection 

of DU (blue), and SID (yellow). .................................................................................... 28 

TABLES 

Page 

Table 1. Six Conditions Used in the Speech Intelligibility, Speaker Identification, and Detection 

of Dramatized Urgency Experiments............................................................................... 8

Table 2. SID Experiment: Confusion Matrix................................................................................ 22 

vi 
vii 

ABBREVIATIONS/ACRONYMS 

DOC Department of Commerce 

DU Dramatized Urgency 

IP Internet Protocol 

ITS Institute for Telecommunication Sciences 

LAN Local Area Network 

LDC Linguistic Data Consortium 

MNRU Modulated Noise Reference Unit 

NTIA National Telecommunications and Information Administration 

NTP Normalized Task Performance 

PCM Pulse Code Modulation 

PDA Personal Digital Assistant 

SID Speaker Identification 

SNR Signal-to-Noise Ratio 

SUSAS Speech Under Simulated and Actual Stress 

TIS Task Induced Stress 

TSID Tactical Speaker Identification Database 

RELATIONSHIPS BETWEEN INTELLIGIBILITY, SPEAKER IDENTIFICATION, 

AND THE DETECTION OF DRAMATIZED URGENCY 

Andrew A. Catellier and Stephen D. Voran1

The systems used for public safety speech communications must be intelligible. It 

is also desirable that they transmit secondary information, such as the attributes of 

a speaker’s voice. This secondary information can allow a user to identify the 

speaker and his or her emotional state. Testing speech communications systems 

for the delivery of intelligible speech is common. Testing for human perception 

of the delivery of this secondary information is less common, though some prior 

work has been done. Building on this prior work, we describe a set of controlled 

laboratory listening experiments. These experiments characterize the relationships 

between speech intelligibility, speaker identification, and the detection of 

dramatized urgency in a speaker’s voice across a range of simulated speech 

processing conditions. The experiment results indicate that for the speech 

processing conditions considered here, detection of dramatized urgency is the 

most robust property, speaker identification is less robust, and speech 

intelligibility is the least robust. 

Key words: human listening tests; intelligible speech; speaker identification; 

speaker stress detection; speaker urgency detection; speech 

transmission system; subjective speech quality tests 

1 INTRODUCTION 

Public safety speech communication systems are designed to carry a message from a speaking 

user (speaker) to a listening user (listener). The most important characteristic of these systems is 

intelligibility (i.e., whether the listener can understand the message). In addition to intelligibility, 

public safety communication systems should aim to successfully transmit secondary information, 

such as attributes of the speaker’s voice. Secondary information allows the listener to identify the 

speaker or to recognize the emotional state of the speaker. 

The ability to identify or confirm the identity of a speaker is called “speaker identification” 

(SID). SID can be particularly important to public safety officials who rapidly communicate 

with each other to accomplish time-critical emergency operations. If speakers can be identified 

implicitly based on transmitted attributes of their voices, the additional overhead associated with 

explicit identification can be avoided (e.g., “This is Officer Roberts speaking.”). Even more 

valuable is the possibility of detecting whether a speaker has lied about their identity. 

Similarly, public safety officials often need to monitor numerous transmissions with only partial 

attention while simultaneously performing other important tasks. If one of many different 

 

1

 The authors are with the Institute for Telecommunication Sciences, National Telecommunications and Information 

Administration, U.S. Department of Commerce, Boulder, CO 80305. 
speakers associated with one of many different transmissions displays a shift in emotional state 

via his or her voice, detecting that shift can be very important. When such a shift is detected 

(e.g., from a neutral tone to a tone of urgency), the listening public safety officials will commit 

full attention to that specific speaking official to provide support and aid. 

This report describes the design, implementation, analysis, and results of a set of three controlled 

laboratory listening experiments. Each experiment uses a set of six different processing 

conditions (e.g., speech coders) that spanned a broad range of perceptual quality. The first 

experiment characterized the relative intelligibility in a sentence context of each processing 

condition. In this document, if intelligibility is mentioned, it can be assumed that we mean 

“intelligibility in a sentence context.” In the intelligibility experiment, the subjects listened to a 

sentence and attempted to repeat it verbatim. The second experiment characterized human SID 

performance for each processing condition. In this SID experiment, listeners learned to recognize 

six different speakers, and then attempted to recognize each speaker’s processed voice. The third 

experiment characterized a listener’s ability to detect dramatized urgency in processed speech. 

In this experiment, listeners attempted to detect one of two dramatized emotional states based on 

the voice characteristics of a speaker. These emotional states are “dramatized neutral” and 

“dramatized urgency” (DU), so we refer to this experiment as “detection of DU.” Together, these 

three experiments characterized the relationships between speech intelligibility, SID, and the 

detection of DU across six speech processing conditions. 

In the sections that follow, we describe related work previously conducted by other researchers. 

We then describe the various speech recordings used in the three experiments, the six speech 

processing conditions, the three experiment designs, the software used, and the main results 

obtained. The results show how intelligibility, SID, and detection of DU vary as a function of the 

six speech processing conditions used. 

2 
2 PREVIOUS RESEARCH 

Much work has been done to develop means for testing speech communications systems. 

However, testing for human perception of the delivery of the secondary information is less 

common. We are not aware of any previous efforts to characterize how the human detection of 

speaker emotional state is influenced by the distortions caused by speech processing associated 

with communication systems. Prior work has been done on the related topics of automatic 

recognition of speaker emotions [1] and automatic speech recognition that is invariant to speaker 

emotions [2]. 

Various studies related to SID have been conducted over the years. In 1963 Compton studied 

human SID abilities for multiple filtered versions of the sustained vowel sound at the end of the 

word “three” [3]. He found that SID can happen with recordings as short as 1/40 of a second. He 

also found that when the pitch of different speakers was closer, those speakers were more easily 

confused. 

Bricker and Pruzansky conducted an experiment where coworkers were asked to identify 

speakers using processed speech recordings. The speakers were familiar to the listeners, and 

pictures were used to aid the identification process [4]. 

Uzdy used two different low-rate vocoders to conduct a SID experiment where listeners were 

familiar with the speakers [5]. His goal was to determine each vocoder’s effectiveness in 

transmitting data pertinent to SID. This goal is similar to our current work. Uzdy discussed the 

importance of adequate listener training and noted that about five hours of training were 

necessary to obtain stable results. 

Schmidt-Nielsen did significant, sustained work on human and machine SID performance, SID 

performance for familiar and new speakers, and the relations between SID performance and 

speech coding distortions [6-10]. In [6] she suggests using a small number of speakers to keep 

within the restrictions of listener memory. Quatieri describes significant work relating machine 

SID to coding distortions in [11]. 

3 
3 SPEECH RECORDINGS 

This section describes the speech recordings used for each experiment. Example speech files 

from the DU portion of this experiment are available at 

<http://www.its.bldrdoc.gov/pub/audio/pubs_talks.php>. 

3.1 Intelligibility 

There are numerous approaches to testing the intelligibility of speech. Some approaches require 

the identification of isolated unrelated words, or leading consonants of rhyming words. In public 

safety communications, as in much of naturally occurring spoken communications, groups of 

words provide context for each other and that can help a listener to properly understand some 

hard-to-hear words. We selected “word intelligibility in a sentence context” as a suitable way to 

assess intelligibility in the present application. To test word intelligibility in sentence context, we 

selected and recorded 20 sentences from current issues of The Wall Street Journal and The New 

York Times. Sentence lengths ranged from 6 to 14 words with a median length of 9 words (e.g., 

“This rebellion has forced banks to reduce bond offerings.”). The sentences were selected to be 

of average complexity and to contain only commonly used words. The sentences are considered 

typical in terms of the amount of context the words within a sentence provide for each other. One 

female and one male speaker recorded each of the sentences. We used studio-grade digital 

recording equipment and a quiet recording room with average noise level below 20 dBA. 

3.2 Speaker Identification 

A search for North American English recordings to use in the SID experiment resulted in the 

selection of the Tactical Speaker Identification Database (TSID), which is available from the 

Linguistic Data Consortium (LDC) [14]. We chose this database because it includes semispontaneous speech, repeated utterances of lists of sentences and digits, and some utterances that 

are recorded by multiple speakers. 

To ensure that the experiment size was manageable within the limits of human memory (as 

suggested in [8]), we decided to select three female speakers and three male speakers from the 

database. Average pitch and voicing strength were determined for each male and female speaker. 

We then looked for male speakers that spoke the same sentences and spanned the full range of 

pitches found in males in the database. Additional considerations in selecting male speakers and 

recordings included minimizing speaker script-reading errors, minimizing microphone handling 

and breath noises, and minimizing microphone overload distortion. We selected three of the four 

female speakers found in the database by maximizing the range of pitches and the quality of 

recordings. 

After speaker selection, we looked for similar digit sequences (of lengths two and four) and 

sentences with similar content spoken by each speaker. These were used to form clips of three 

lengths: short, medium, and long. Semi-spontaneous speech was used for training purposes. 

4 
3.3 Detection of Speaker “Stress” 

In general, we are interested in listener detection of speaker “stress.” But the term “stress” is 

subjective, and covers a wide range of circumstances and resulting speech signals. For speech 

signals, objective refinement of the term “stress” and quantification of stressor levels is enabled 

through the use of known acoustic correlates. These include changes in level, tempo, pitch, and 

formants [1, 2, 13, 14]. 

3.3.1 Task Induced Stress 

Previous work to develop and test automatic speech recognition that is invariant to speaker 

emotions resulted in the Speech under Simulated and Actual Stress (SUSAS) recorded speech 

database [13, 15]. One portion of the SUSAS database involved a male helicopter pilot recording 

isolated words in neutral (helicopter on the ground and running) and task (pilot flying helicopter) 

situations. Another portion included one male and one female speaker recording isolated words 

in neutral (no task) and computer-graphics based “dual tracking” task situations. When 

comparing the task recordings with the neutral recordings, only a minor sense of distraction is 

evident. We call this Task Induced Stress (TIS), and we used some of these recordings as the 

basis for a portion of this experiment. The experiment revealed that the detection of TIS is a very 

difficult task, independent of the speech processing condition. Because of this, the TIS portion 

of the experiment is not addressed in the remainder of this report. 

3.3.2 Dramatized Urgency 

Since urgent speech is not uncommon in public safety communications, speakers conveying 

urgency were an important part of this experiment. It would not be ethical to subject speakers to 

events (e.g., physical dangers) that could create a true sense of urgency. Recording speakers 

confronted by naturally occurring urgency-inducing events was not a practical option, but this 

might be considered for potential future work. We elected to create recordings of DU. 

We monitored public safety communication channels and transcribed messages between public 

safety personnel to use as scripts. Messages selected ranged in length from 2 words to 21 words 

with a median length of 9 words (e.g., “We have two children still trapped under the bus.”). For 

comparison purposes, the scripts also included the isolated words of the TIS recordings. 

One female and one male speaker recorded the DU scripts. We used studio-grade digital 

recording equipment and a quiet recording room with average noise level below 20 dBA. Each 

speaker read the scripts while verbally dramatizing two different situations: a non-urgent 

situation (neutral) and a situation requiring an urgent response (DU situation). We activated a set 

of rotating mirrored red and blue strobe lights to provide an unmistakable visual indication of 

when the speakers should dramatize urgency. A total of 16 different messages were used in the 

experiment. 

5 
3.3.3 Acoustic Correlates in Dramatized Urgency 

We analyzed the DU recordings and identified several acoustic correlates. The level of DU 

speech increased (relative to neutral speech) by an average of 6.2 dB for the male speaker, and 

8.0 dB for the female speaker. However, note that this level increase was not directly available to 

listeners because it was removed via level normalization. It may have been indirectly available if 

it was accompanied by audible sounds of increased speaking effort. 

The two speakers responded oppositely in terms of tempo. The male speaker increased his 

talking tempo slightly in DU, so his average message duration decreased from 2.86 to 2.68 

seconds. The female lengthened certain words for emphasis and thus decreased her tempo. Her 

average message duration increased from 2.73 to 3.01 seconds. 

The mean pitch of the male speaker increased from 134 Hz (neutral) to 148 Hz (DU), while the 

standard deviation increased from 21 to 23 Hz. For the female speaker, the mean pitch increased 

from 211 to 249 Hz, and standard deviation increased from 18 to 38 Hz. The pitch histograms in 

Figure 1 show all four of these results. We also observed changes in formant structure for both 

speakers. 

Figure 1. Pitch histograms for four cases as labeled. 

6 
The increases in mean pitch and pitch variation found in our DU recordings are qualitatively 

consistent with those found in cockpit voice recordings of a real stressful and urgent situation. 

These recordings document the voices of a pilot and copilot both when relaxed, and in the 

minutes before their aircraft crashes [14]. Whether or not DU is a good surrogate for true 

urgency will likely depend on numerous factors including individual speakers' physical and 

psychological characteristics and the details of the urgent situation. 

All recordings for all experiments were resampled to a rate of 8,000 samples per second using 

the “PCM filter” option (160 to 3640-Hz bandpass filtering) provided in [16]. The level of each 

recording was then normalized to –26 dB, relative to clipping using tools from [16]. Next, the 

recordings were passed through software to implement various speech processing conditions. 

7 
4 SPEECH PROCESSING CONDITIONS 

The goal of the experiments was to find the relationships between speech intelligibility, speaker 

identification, and the detection of dramatized urgency. The usefulness and robustness of these 

relationships is greatest when they span the widest possible range of these parameters. Thus, six 

speech processing conditions were chosen to provide the widest possible range of experimental 

results. Table 1 summarizes the six conditions. 

Table 1. Six Conditions Used in the Speech Intelligibility, Speaker Identification, and Detection 

of Dramatized Urgency Experiments 

Condition (C) Description 

C1 Null (no further processing) 

C2 Low rate speech coding 

C3 Very low rate speech coding 

C4 MNRU, Q = 6 dB SNR 

C5 Low rate speech coding with bit errors 

C6 C5+Severe Packetization Impairments+C5 

C1 involves no additional processing and thus provides a best-case reference point for all three 

tasks. In C4, Modulated Noise Reference Unit (MNRU) [19] software produces multiplicative 

(speech-correlated) noise resulting in an active speech SNR of 6 dB. This is a standardized 

reference condition that can allow one to build relationships to other experiments that also 

include the MNRU. 

The remaining conditions use three different narrowband (4-kHz nominal) speech codecs 

specified in standards or proposed standards for low bit-rate digital communication in the 

presence of acoustic background noise. These codecs simulate frequency-dependent voicing 

strength by adaptively mixing periodic and aperiodic excitation signals. Since communications 

are increasingly likely to travel through some IP network it is important to model and examine 

possible effects. For C6, three simulated communication systems are concatenated. The first and 

last are the same as C5 (speech encoding, bit errors in the transmission channel, then speech 

decoding). The middle system consists of packetization of the speech samples followed by the 

deletion of randomly selected packets and the insertion of an equal number of empty packets at 

different random locations. A packet loss concealment algorithm is used to extend previous 

speech samples into these inserted empty packets. 

8 
The speech processing conditions are certainly relevant to public safety speech communication 

systems. But evaluating the conditions is not the primary goal of these experiments. Rather the 

conditions are tools that enable the experiments to yield relationships between speech 

intelligibility, speaker identification, and the detection of dramatized urgency. 

After creating recordings for each condition, the active speech level of each recording was again 

normalized to –26 dB relative to clipping. 

9 
5 EXPERIMENT DETAILS 

This section describes how each experiment was conducted. Evaluation of speech intelligibility, 

SID, and the detection of DU each required separate laboratory procedures. Each experiment 

was automated and used a different user interface. The speech intelligibility experiment and the 

detection of DU experiment used the same listeners—their time in the lab was split between the 

two experiments. The SID experiment was conducted about six months later, and used a different 

set of listeners. 

5.1 Speech Intelligibility 

Twenty-four randomly-selected listeners participated in the experiment. Sixteen were male, eight 

were female, estimated ages ranged from 20’s to 60’s with a mean estimated age of 

approximately 40, all were fluent in English, two reported slight hearing losses, and none were 

familiar with the technical details of the experiment. Listeners participated one at a time in a 

sound-isolated room where the average background noise level was below 20 dBA. The 

recordings were played over a powered monitor speaker with a single full-range four-inch driver. 

Listeners could adjust the listening level to their preferred level at any time. 

In the experiment, listeners heard a recorded sentence and were asked to repeat it back. These 

responses were recorded and later evaluated for the number of correct words repeated. Listeners 

could not proceed until the entire sentence was played, and they were not allowed to replay any 

sentence. Progress through the experiment was controlled through a graphical interface on a 

PDA supported by a wireless LAN connection. 

The experiment started with a practice session containing four trials. This session familiarized 

listeners with their task and with the procedures. Following this practice session, each listener 

then heard 24 sentences (4 per condition) and the sentences used with each condition were varied 

in a balanced way as the experiment progressed. The result was 96 intelligibility trials per 

condition (each sentence was used 4 times per condition, but only once per listener), for a total of 

576 trials. Each listener heard the recordings in a different random order. After the experiment, 

several different statistical tests showed that no single listener was an outlier in this speech 

intelligibility task. Responses were recorded and later evaluated to compute the number of 

correct words repeated. 

A second version of this experiment was later given to six additional listeners. This version used 

the same speech recordings, speech processing conditions, and general procedures. It differed 

from the original experiment only in that listeners were allowed to hear the recordings as many 

times as they wished. After each playing of a recording, listeners were asked to repeat the 

sentence as they heard it or to report that no words were understood. Responses were recorded 

and later evaluated for three quantities: the number of correct words repeated after the first 

playing, the number of correct words repeated after the final playing, and the total number of 

plays. On some occasions, a subject would fail to provide any response after a playing and this 

trial was scored as zero words correct. 

10 
This second version of the intelligibility experiment does not conform with typical approaches, 

but it does more closely parallel the SID and detection of DU experiments. In each of these, 

listeners were allowed to play recordings as many times as they wish. 

5.2 Speaker Identification 

The SID experiment design and procedures were refined several times using feedback from 

subjects who participated in early versions of the experiment. The final design included seven 

different parts. Three were actual experimental test sessions where data is collected, and four 

were supporting parts that were preliminary or tutorial in nature. 

5.2.1 Listeners 

Twenty-five randomly selected listeners participated in the experiment. Fifteen were male and 

ten were female. Their ages ranged from approximately 37 to 64 with a mean age of 49. None of 

the listeners were familiar with the technical details of the experiment. Listeners participated one 

at a time in a sound-isolated room where the average background noise level was below 20 dBA. 

Recordings were played over a powered monitor speaker with a single full-range four-inch 

driver. Listeners could adjust the listening level to their preferred level at any time throughout 

the experiment. The experiment, including all training and testing, took listeners from 45 to 90 

minutes to complete, and the average completion time was just under one hour. 

The randomly selected listener pool included two listeners with hearing aids and one listener 

who reported deafness in one ear. After careful consideration described in Section 6.2.1, we 

elected to include the data from these three listeners in the overall experiment results. 

The experiment administrator received many hours of exposure to both undistorted and distorted 

recordings from the six speakers. After this incidental training, the experiment administrator also 

served as a listener. As described in Section 6.2.1, his results are not included in the overall 

experiment results. 

5.2.2 Training and Quiz Session 

The experiment starts with a session where the listener assigns a face and a name to each of the 

six different speakers. This session is provided to allow the experiment to better simulate the 

actual conditions under which listeners most often identify speakers that they cannot see. That is, 

listeners typically can reference a name, face, or both in memory when identifying speakers who 

cannot be seen. 

To accomplish this goal, the SID experiment was controlled interactively through a computer 

program. Figure 2 shows the window presented during listener training. The middle of the 

window on the computer screen has one button for each speaker (e.g., “Speaker 1”, “Speaker 2”). 

The listener presses a button associated with a speaker. The program plays several sentences 

11 
spoken by this speaker, and allows the listener to select a name and portrait2

 (or identity) that 

seems appropriate for the speaker from the options listed on the left side of the screen. From 

now on, in addition to playing several sentences spoken by this speaker, the selected identity is 

displayed on the right side of the screen whenever the listener presses this speaker’s button. 

Figure 2. Speaker identification training interface for listeners. 

After hearing all six speakers and assigning a name and portrait to each, listeners take a short 

quiz. This quiz helps the listener check their knowledge of the speakers and become confident in 

their ability to recognize each speaker. The initial quiz state is shown in Figure 3. During the 

quiz, the listener hears a sentence. The listener identifies the speaker by clicking on the identity 

that he believes belongs to the currently playing speaker. The program immediately displays the 

selected identity prominently on the right side of the screen, as shown in Figure 4. If satisfied 

with the selection, the listener can confirm his choice; if not, he can make another selection. 

Upon confirmation, feedback indicating whether or not his selection is correct is displayed, as 

 

2

 The portraits shown in Figures 2-5 were used under Creative Commons license; attributions listed in the appendix. 

12 
shown in Figure 5. The process repeats for all six speakers. Then the listener chooses to either 

continue training or proceed to the test sessions. 

Figure 3. Speaker identification quiz interface for listeners—initial state. 

13 
Figure 4. Speaker identification quiz interface for listeners—after listener selection. 

14 
Figure 5. Speaker identification quiz interface for listeners—correct answer display. 

5.2.3 Experimental Test Session 1—Sentences 

The first experimental test session uses two sentences from each speaker. There are six speakers 

and six conditions for a total of 2 × 6 × 6 = 72 trials in the session. One sentence is the same for 

all six speakers: “Don't ask me to carry an oily rag like that.” The second sentence differs for 

every speaker. The recordings used in this session range from approximately 1.7 to 2.6 seconds 

in length (8 to 13 syllables in length) with a mean value of about 2.2 seconds (about 11 

syllables). 

This experimental test session was presented to the listener in a fashion very similar to the 

aforementioned quiz session. One of the 72 available recordings was played back from the 

beginning of a randomized list. The randomized list was unique for each listener to prevent any 

potential order effects. The listener was asked to identify the speaker of the recording, and select 

the correct identity out of the six shown on the left side of the window, on a screen similar to 

Figure 3. Once clicked, the selected identity displayed prominently, similar to Figure 4, and the 

listeners were allowed to move on to the next recording. However, the listeners were allowed to 

15 
select a different identity or replay the recording as many times as necessary. Unlike the quiz 

session, the listeners were not notified about the accuracy of their selection—the software simply 

moved on to the next recording in the randomized list after identity selection was confirmed. 

5.2.4 Experimental Test Sessions 2 and 3—Digits 

A short reminder session is provided before experimental test sessions 2 and 3. The six chosen 

identities are displayed on the left side of the window. The listener is instructed to listen to each 

speaker at least once before moving on to the next experimental test session. By clicking any of 

the portraits, the listener can hear a recording of the corresponding speaker that is similar to 

those in the upcoming session. The listener can spend as much time in this reminder session as is 

desired, and the software requires that the listener listen to each speaker at least once. Once the 

reminder session is complete, experimental test sessions 2 and 3 are administered exactly as 

session 1 was. 

The second experimental test session uses recordings that contain four spoken digits (e.g., “three 

six nine eight”). Each of the six speakers was associated with four recordings (of four digits 

each), so the session contained a total of 4 × 6 × 6 = 144 trials. The recordings in this session 

range from approximately 1.3 to 1.9 seconds in length (4 to 5 syllables in length) with a mean 

value of about 1.6 seconds (about 4.4 syllables). There were 15 unique “4-digit” sequences—we 

did not control the recording of this database, so within the speakers only 15 unique 

combinations were available. With one exception, all speakers recorded a completely different 

sub-set of these 15 sequences. Pairs of these 4-digit sequences often have two or three digits in 

common. Because of these similarities and subtle differences, content-based SID would be 

extremely difficult (e.g., noticing which 4-digit sequences are associated with Speaker 1). 

The third session uses recordings that contain pairs of spoken digits. All six speakers provided 

the exact same four recordings: “five two”, “six zero”, “six three”, and “eight zero”. Thus, in this 

session, content is identical across speakers, and content-based SID is not possible. Here again, 

the session includes 144 trials. The recordings used in this section range from approximately 0.6 

to 0.8 seconds in length (2 to 3 syllables in length) with a mean value of about 0.7 seconds 

(about 2.5 syllables). 

The combined number of trials for all three experimental test sessions is 72 + 144 + 144 = 360. 

5.3 Detection of Dramatized Urgency 

This experiment used the same twenty-four randomly-selected listeners that participated in the 

speech intelligibility experiment (see Section 5.1). Recordings were played over a powered 

monitor speaker with a single full-range four-inch driver. Listeners could adjust the listening 

level to their preferred level at any time. Experiment progress and data collection were controlled 

through a graphical interface on a PDA supported by a wireless LAN connection. Listeners first 

participated in a practice session to familiarize them with the task and the procedure. 

Listeners heard a recording and responded to the prompt “Please select the talker’s stress or 

urgency level.” Response options in each of these binary forced-choice trials were “Low” (the 

16 
correct answer for neutral recordings) and “High” (the correct answer for TIS and DU 

recordings). Listeners could respond at any time once a recording had started to play, and could 

restart the playback at any time. In this manner, each listener could proceed at an individualized 

pace through the experiment. Each listener heard 192 trials for a total of 4,608 (192 trials × 24 

listeners) DU detection data points. 

17 
6 RESULTS 

Throughout this section and the next, we report results in terms of either fraction correctly 

identified or normalized task performance (NTP). We introduce the NTP scale because it enables 

a more direct comparison of the results from the three experiments. On the NTP scale, a value of 

1 indicates perfect information from the listeners in the experiment, and a value of 0 indicates no 

information from the listeners. This is true for the intelligibility, SID, and detection of DU 

experiments. 

6.1 Speech Intelligibility 

In the intelligibility experiment, NTP is simply the fraction of words correctly repeated by a 

listener. If 100 percent of the words were repeated correctly, an NTP value of 1 would be the 

result. If none of the words were repeated correctly, then an NTP value of 0 would be the result. 

Figure 6 shows the mean NTP values for each of the six speech processing conditions, after 

averaging over all listeners and all messages for each condition. The data points shown by the 

solid line describe the main intelligibility experiment (24 listeners, replaying recordings not 

allowed). The data points shown by the dashed and dotted lines correspond to results from the 

second version of the intelligibility experiment (six listeners, replaying recordings allowed). The 

dashed line shows results that were calculated after the first play and the dotted line shows 

results that were calculated after the final playing. The 95-percent confidence intervals (CI) for 

these three cases overlap and are omitted from this figure for clarity. 

18 
Figure 6. Intelligibility experiment: NTP values for three cases—solid line for single play 

experiment, dashed line for the first play, and dotted line for the last play in the multiplay 

experiment. 

Figure 6 shows that allowing replays does improve NTP by a small amount (the dotted line is 

above the solid line). The larger increases in NTP are associated with cases of medium speech 

intelligibility (C4 and C5) where additional plays apparently can help at least some listeners with 

the task. The smaller increases in NTP are associated with cases of very high (C1, C2, and C3) 

and very low intelligibility (C6). It seems that in these limiting cases, additional plays provide 

limited advantage. The average number of plays generally increases with condition number from 

just over one (1.3 in C1) to two (2.0 in C6). 

While allowing replays does improve NTP by a small amount this does not change the general 

trends as we move from C1 to C6. That is, all three lines in Figure 6 show a similar general 

trend: a gentle decrease in NTP followed by an abrupt drop. Based on this similarity of shape we 

argue that the final conclusions about relative robustness in Section 7 are not highly sensitive to 

the choice between these two intelligibility testing approaches (single play of each recording 

versus unlimited playing of each recording). The main intelligibility experiment (with no replays 

allowed) has much more data than the secondary intelligibility experiment (24 listeners vs. 6 

listeners) and thus we focus on that experiment from this point forward in this report. 

19 
Figure 7 shows the mean NTP values and 95 percent CI for each of the six speech processing 

conditions, after averaging over all listeners and all messages for each condition. Note that as we 

move from C1 to C6, NTP drops monotonically from 0.95 to 0.11. This is an NTP drop of 0.84. 

Figure 7. Intelligibility experiment: NTP mean and 95% CI for word intelligibility in sentence 

context. 

6.2 Speaker Identification 

In the SID experiment, 360 trials were administered to 25 listeners in the main pool. This gives 

9,000 data points. Each data point is one SID, which can be either correct or incorrect. Using this 

view, the data is binary in nature and can be modeled using the binomial distribution. In the 

binomial model, the maximum likelihood estimate for the probability of correct identification is 

simply as shown in (1). 

total identificaofnumber tions

number of correct identifications

pˆ = (1) 

20 
The 95% CI for the estimate is calculated as given in [18]. We report as the fraction 

correctly identified in Section 6.2.1 and Section 6.2.2, and we report the 95% CI for the estimate 

 as well. 

pˆ pˆ

pˆ

Sections 6.2.1 through 6.2.3 discuss the results of the SID experiment in three different ways—

broken up by listeners, speakers, and then more directly by speech processing condition. When 

the data is broken up by listener, it is clear that each listener scored better than would be likely if 

he was randomly guessing. While there was a general decreasing trend in NTP, there were no 

statistically significant differences among sessions. Therefore, results broken up by session (or 

clip length) are not discussed. Some speakers are easier to recognize than others, and those not 

easily recognized are often confused with other speakers. 

6.2.1 Listeners 

Figure 8 shows the fraction of correct identifications and the associated 95% CI for the 25 

listeners. Listeners are numbered and sorted by increasing fraction of correct identifications. The 

mean fraction of correct identifications over all listeners is 0.662, and 20 of the 25 listeners have 

overall correct identification fractions between 0.59 and 0.81. The people who utilized hearing 

aids have listener numbers 14 and 16. Listener 16 was also a non-native speaker. The listener 

who reported deafness in one ear is listener number 20. Figure 8 shows that none of these three 

listeners is an outlier. Thus, all three are retained in our data pool. 

Figure 8. SID experiment: Mean fraction correctly identified by listener and 95% CI for each 

listener. 

21 
Out of the three listeners whose first language was not English, only one seemed to be at a 

disadvantage (listener 10). The other two non-native English speakers (listeners 13 and 16) 

placed close to the fraction-correct mean among all listeners. We elected to retain all three of 

these listeners in our data pool. 

Not shown in Figure 8 is the experiment administrator, who received a great deal more training 

(more than 20 hours on speech distorted under all conditions). The experiment administrator was 

significantly more accurate with a 0.98 fraction of correct identifications. This is an indication 

that additional training can have a positive effect on SID performance. It’s possible that the 

experiment administrator memorized to whom many of the recordings belonged, but it’s also 

possible that over time he learned how each condition affected speech. The latter possibility may 

be relevant, as it is possible for listeners to become accustomed to adverse communication 

conditions. Once again, the experiment administrator's results were not included in the overall 

experiment results. 

6.2.2 Speakers 

Our selection of speakers had some interesting properties. The males had average pitches of 92, 

105, and 111 Hz, and Male 3 had a slight Southern accent. The females had average pitches of 

103, 104, and 107 Hz. Female 1 had a Midwestern accent, Female 2 had a Southern accent and 

Female 3 had a heavy Ecuadorian accent. The task of distinguishing among the three females is 

made easier (relative to the task of distinguishing among the three males) by very pronounced 

accents despite their small average pitch spread relative to that of the males. 

Table 2 is the confusion matrix for the SID task for these six speakers averaged across all clips, 

conditions, and listeners. Each row is associated with one speaker, and each column is associated 

with the listener votes. “M” indicates male, “F” indicates female. Shaded cells indicate the 

fraction of correct SID, unshaded cells indicate fractions of confused SID. For example, the top 

left entry indicates that 67 percent of the clips from Male 1 were identified as coming from Male 

1. The next entry to the right indicates that 22 percent of the clips from Male 1 were identified as 

coming from Male 2. Similarly, the next entry to the right indicates that 11 percent of the clips 

from Male 1 were identified as coming from Male 3. 

Table 2. SID Experiment: Confusion Matrix 

Listener Selected Speaker 

M1 M2 M3 F1 F2 F3 

M1 0.670 0.220 0.110 0.000 0.000 0.000 

M2 0.150 0.570 0.220 0.010 0.030 0.030 

M3 0.120 0.340 0.540 0.000 0.000 0.000 

F1 0.000 0.003 0.001 0.650 0.190 0.160 

Actual Speaker 

F2 0.000 0.004 0.001 0.170 0.740 0.080 

F3 0.001 0.003 0.005 0.070 0.120 0.800 

The confusion matrix shows that Female 3 is easier to identify than Female 2, who in turn is 

easier to identify than Female 1 (correct identification fractions of 0.80, 0.74, and 0.65, 

22 
respectively). Male 1 (with a correct identification fraction of 0.67) and Female 1 are close to the 

same difficulty, and Males 2 and 3 (fractions of 0.57 and 0.54, respectively) are both more 

difficult. The task of distinguishing among the males is difficult because Males 2 and 3 sound 

very similar (despite a slight Southern accent present in Male 3). In fact, the matrix shows that 

the greatest levels of confusion are between Males 2 and 3, though confusions between Male 1 

and Male 2, and confusions between Female 1 and Female 2, are not far behind. 

As Schmidt-Nielsen notes, listeners perform the SID task more efficiently with familiar, or 

distinctive speakers [6]. Our results are consistent with prior research. The two male speakers 

who had the smallest fraction of correct identifications were also often expressed as perceptually 

similar by listeners during the experiment. While the average pitch difference between the two 

easily confused male speakers is greater than the pitch spread among all female speakers, the 

female speakers were arguably more distinctive due to their regional accents. The speech 

processing conditions used in these experiments preserve pitch in the general sense, though the 

finer nuances of pitch trajectories will sometimes be lost. 

Listeners received 15 minutes of training with these 6 unfamiliar speaker voices on average. 

Listeners were allowed to continue the training process as long as they wished. Many of the SID 

trials involved recordings in which the voice was greatly distorted. Thus, this amount of 

confusion is not unexpected. It is interesting to note that only Male 2 is ever perceived to be a 

female; all three females are confused for males, but only rarely. 

The difficulty of the SID task is broken down by speaker and by condition in Figure 9. Males 1, 

2, and 3 are shown with red circles, orange stars, and dark gray downward-pointing triangles and 

lines, respectively. Females 1, 2, and 3 are shown with blue upward-pointing triangles, green 

squares, and purple diamonds and lines, respectively. With few exceptions, easier-to-identify 

speakers tend to be easier for all six conditions, and harder-to-identify speakers tend to be harder 

for all six conditions. The major exception is female 1 who is one of the easiest-to-identify 

speakers when heard over C1, C2, and C4, but is one of the hardest-to-identify speakers when 

heard over C5 and C6. 

23 
Figure 9. SID experiment: Fraction correctly identified and 95% CI by speaker and condition. 

6.2.3 Speech Processing Conditions 

A main goal of this work is to quantify how the listener SID performance is influenced by the six 

speech processing conditions. Each of the six conditions described in Table 1 was used for a total 

of 1,500 SID trials. For each condition, these 1,500 trials used the same 60 recorded speech files, 

and the same 25 listeners as well. This balance allows us to compare SID results for the six 

conditions directly, as Figure 10 shows. 

For SID, the transformation from estimated probability of correct identification to NTP is 

shown in (2). 

pˆ

⎟

⎠

⎞ ⎜

⎝

⎛

−×=

6

1 ˆ 56 NTP p , ˆ 1

6

1

p ≤≤ (2) 

24 
Because six responses are possible in this experiment, a listener making no effort and giving 

strictly random responses could have an average fraction of correct identifications of 

6

1 . Thus, 

6

1 corresponds to no information from a listener, and (2) maps 

6

1 to an NTP value of 0. On the 

other hand, perfect SID corresponds to an NTP value of 1. 

The transformation given in equation 2 is not defined for values of pˆ that are less than 

6

1

(worse than guessing). While such values are theoretically possible, a meaningful interpretation 

is not obvious and would likely depend on additional factors. In this experiment no listeners 

exhibited performance worse than guessing. We expect that such results would be an extremely 

rare aberration and thus we do not consider the restriction in equation 2 to be a significant 

limitation. 

Figure 10. SID experiment: NTP mean and 95% CI by condition. 

Note that as we move from C1 to C6, the SID NTP drops steadily from 0.69 to 0.41. This is an 

NTP drop of 0.28. 

25 
6.3 Detection of Dramatized Urgency 

For each trial in a detection experiment, three outcomes are possible: correct detection, false 

alarm (low urgency reported as high urgency), and miss (high urgency reported as low urgency). 

Given the binary nature of the data (correct or not correct), the maximum likelihood estimate for 

the probability of correct detection and the 95% CI for that estimate are calculated as given in (1) 

and [18]. As with SID, we report detection of DU results in terms of the NTP scale. In this case, 

that scale is defined by (3). 

 ⎟

⎠

⎞ ⎜

⎝

⎛

−×=

2

1 2NTP pˆ (3) 

Because two responses are possible in this experiment, a listener making no effort and giving 

strictly random responses could have an average fraction of correct identifications of 

2

1 . (3) 

maps this to an NTP value of 0. 

Figure 11 shows the mean NTP values and 95% CI for each of the six speech processing 

conditions, after averaging over all listeners and all messages for each condition. Figure 11 

shows that as one progresses from C1 to C6, the NTP for detection of DU in messages drops 

from 0.76 to 0.58 (an NTP drop of 0.18). We also found that across the conditions, the false 

alarm rate tends to be lower than the miss rate. The false alarm rates generally fall into the range 

0.05 to 0.10, while the miss rates generally span the range 0.10 to 0.35. In other words, 

detection-of-DU errors are less frequent when speakers are in the neutral state, and more 

frequent when speakers are in the DU state. 

26 
Figure 11. Dramatized Urgency Experiment: NTP mean and 95% CI for detection of dramatized 

urgency. 

27 
7 COMBINED RESULTS AND DISCUSSION 

This section contains the results from 6.1, 6.2.3, and 6.3 combined. This allows one to compare 

the mean intelligibility, SID, and detection of DU results across the six speech processing 

conditions, consistent with the overall goal of this work. These combined results are given in 

Figure 12. The green line shows NTP mean and 95% CI for word intelligibility; the blue line 

shows mean and CI for detection of dramatized urgency; the yellow line shows mean and CI for 

SID. All three results generally decrease as one progresses from C1 to C6. 

Figure 12. NTP mean and 95% CI for word intelligibility in a sentence context (green), detection 

of DU (blue), and SID (yellow). 

In the following we compare these decreases in intelligibility, DU, and SID NTP from C1 to C6 

instead of comparing the raw NTP values of those three quantities for each speech processing 

condition. We believe that comparing these decreases leads to conclusions that are more likely 

to generalize beyond this specific set of experiments. It is clear that conclusions based on the 

raw NTP values do not generalize beyond this specific set of experiments because the raw NTP 

values can be very strongly influenced by experiment design. 

28 
If the SID experiment had used 24 speakers instead of 6 speakers, that task would have been 

much harder and the SID results curve in Figure 12 would undoubtedly be shifted downward 

dramatically, likely resulting in saturation with an NTP very close to 0 for all six speech 

processing conditions. Conversely, if the SID experiment had used just one female and one male 

speaker, that task would have been much easier and the SID results curve would have 

undoubtedly shifted upward dramatically, likely resulting in saturation with an NTP near 1 for all 

six speech processing conditions. Such dramatic shifts can saturate the NTP scale and this can 

prevent us from finding the information we seek in this work: how are intelligibility, DU, and 

SID influenced by the six speech processing conditions? 

Figure 12 shows that our experiment designs have been successful in that none of the three 

curves saturates at the top or bottom of the NTP scale. In light of the discussion above, 

statements about SID NTP relative to DU NTP for C3, for example, are specific to the 

parameters of these experiments (e.g., six talkers in SID, two levels of DU, and a host of other 

experiment parameters). On the other hand, an examination of the decreases in intelligibility, 

SID, and DU NTP as we progress from C1 to C6 leads to somewhat more general conclusions 

about the relative robustness of intelligibility, SID, and DU to the distortions induced by the six 

speech processing conditions. While these decreases in NTP may also be influenced by 

experiment design, we argue that they must be much less sensitive than the raw NTP values. 

Figure 12 shows the intelligibility results decrease most abruptly (from 0.95 to 0.11 for a 

decrease of 0.84), and the detection of DU results decrease most gently (from 0.76 to 0.58 for a 

decrease of 0.18). The SID results show a decrease that is between the other two (from 0.69 to 

0.41 for a decrease of 0.28). If we compare the NTP decrease for SID with the NTP decrease for 

intelligibility (0.28 compared with 0.84), we can conclude that the SID is 3.0 times (0.84/0.28) 

more robust to the distortions created by the speech processing conditions than intelligibility is. 

Similarly, comparison of the NTP decrease for detection of DU with the NTP decrease for 

intelligibility (0.18 compared with 0.84) indicates that the detection of DU is 4.7 times 

(0.84/0.18) more robust to the distortions created by the speech processing conditions than 

intelligibility is. 

These are the final relationships to be extracted from these experiments. They suggest that if a 

speech communication system is well represented by the speech processing conditions used in 

these experiments and its distortions are such that word intelligibility NTP has not decreased 

significantly (a very natural requirement for any useable system) then those distortions are 

unlikely to have caused SID and DU NTP to have decreased significantly. 

Laboratory experiments like those described in this report are important because they provide a 

level of control over speaking, listening, and speech processing conditions that allows one to 

extract meaningful results. This would not be possible in a typical field environment. While 

laboratory experiments are essential to research progress, it is also true that the laboratory is 

often less realistic than the field environment. 

One factor to consider in this regard is the consequence of various types and levels of 

background sounds at speaker and listener locations. It is clear that these background sounds can 

have negative effects, but they might also aid in SID (e.g., when it is known that Officer Roberts 

is at the coffee shop and Officer Smith is at a subway station, the corresponding background 

29 
sounds could help with SID). They might also enable detection of urgency in speakers’ voices. 

Examples of background sounds that may indicate a situation in which an officer could be 

exhibiting urgency in his voice may be screeching tires or even gunfire. 

The relationship between dramatized urgency and the actual emotional signatures found in the 

voices of public safety officials is also of great interest. Dispatchers and officials who deal with 

urgent, catastrophic or tragic events on a routine basis may show less emotional variation in their 

voices than the general public would. However, it is possible that even officials with the calmest 

demeanor may find themselves in situations where they would exhibit urgency in their speech. 

Our results show that if this were to occur, the listener would likely be able to detect the sound of 

urgency. If intelligibility needed to be enhanced, officials could speak more slowly and clearly, 

but this could have a negative effect on SID, in that the speaker’s normal cadence and speaking 

rhythm would be altered. 

An additional issue centers on SID of familiar and unfamiliar speakers. Certainly years of 

professional association can cause voices and speaking styles to be very familiar, even under 

adverse conditions. This could lead to SID rates higher than those found in this laboratory study 

that uses unfamiliar speakers and a relatively short training or “acquaintance” process. On the 

other hand, many or most public safety officials communicate with far more than six other 

officials on a regular basis. This could make the SID task more difficult. How these two 

competing effects might balance out could only be determined through additional research 

efforts. 

We designed the DU and SID experiments to have minimal complexity and yet provide useful 

basic DU and SID information. It is clear that one could design a large number of more intricate, 

more complex, or more specific experiments to explore the issues discussed in this section and a 

host of other associated issues. As experiments become more complex and specific, it becomes 

increasingly important to pay close attention to the balance between control and realism so that 

experiment results are of maximum utility and applicability.

30 
8 ACKNOWLEDGEMENTS 

This work was funded by the Department of Homeland Security Office of Interoperability and 

Compatibility, through the National Institute of Standards and Technology Office of Law 

Enforcement Standards. The work was conducted at the Institute for Telecommunication 

Sciences under the supervision of J.R. Bratcher. 

The authors of this report wish to express deep gratitude to Ryan Buffington and Alicia DeMino 

for implementing and executing the intelligibility and DU experiments, including database 

identification and acquisition, recording speech samples, software development, listener 

recruitment, and experiment administration. 

We also wish to thank the anonymous listeners from the US Department of Commerce Boulder 

Labs who participated in these experiments. 

31 
9 REFERENCES 

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[2] S. Bou-Ghazale and J. Hansen, “A comparative study of traditional and newly proposed 

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[7] A. Schmidt-Nielsen and K.R. Stern, “Recognition of previously unfamiliar speakers as a 

function of narrow-band processing and speaker selection,” The Journal of the Acoustical 

Society of America, vol. 79, no. 4, pp. 1174–1177, 1986. 

[8] A. Schmidt-Nielsen, “A test of speaker recognition using human listeners,” in Proc. 1995

IEEE Workshop on Speech Coding for Telecommunications, Annapolis, Maryland, 

Sep. 1995, pp. 15–16. 

[9] A. Schmidt-Nielsen and D.P. Brock, “Speaker recognizability testing for voice coders,” in 

Proc. 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, 

Atlanta, Georgia, May 1996, vol. 2, pp. 1149–1152. 

[10] A. Schmidt-Nielsen and T.H. Crystal, “Human vs. machine speaker identification with 

telephone speech,” in Proc. 5th International Conference on Spoken Language Processing, 

Sydney, Australia, Nov. 1998, vol. 2, pp. 221–224. 

[11] T. F. Quatieri, Discrete-Time Speech Signal Processing, Principles and Practice, Chapter 

14, Upper Saddle River, NJ: Prentice Hall, Inc., 2002. 

[12] Tactical Speaker Identification Database, Available at http://www.ldc.upenn.edu. 

[13] H. Steeneken and J. Hansen, “Speech under stress conditions: Overview of the effect on 

speech production and on system performance,” in Proc. 1999 IEEE International 

Conference on Acoustics, Speech, and Signal Processing, Mar. 1999, vol. 4, pp. 2079–2082. 

32 
[14] R. Ruiz, E. Absil, B. Harmegnies, C. Legros, and D. Poch, “Time-and spectrum-related 

variabilities in stressed speech under laboratory and real conditions,” Speech 

Communication, vol. 20, no. 1-2, pp. 111–129, Nov. 1996. 

[15] Speech under Simulated and Actual Stress Database, Available at 

http://www.ldc.upenn.edu. 

[16] ITU-T Recommendation G.191, Software tools for speech and audio coding standardization, 

Geneva, 2005. 

[17] ITU-T Recommendation P.810, Modulated noise reference unit (MNRU), Geneva, 1996. 

[18] N. Johnson, S. Kotz, and A. Kemp, Univariate Discrete Distributions, New York: Wiley, 

second edition, 1992, p. 129. 

33 
34 
35 

APPENDIX: PHOTO ATTRIBUTIONS 

All photos used in the SID portion of this experiment were under a Creative Commons license at 

the time of download. Attributions are listed below. The artist’s name or alias is listed first and 

is followed by their flickr <http://flickr.com> username in parentheses. If no alias is listed, the 

username of the account where the picture was downloaded at the time of download is listed.

Johannes Henseler (frischmilch flickr) 

Vladislav Sabanov (willgame on flickr) 

Katie Tegtmeyer (Katie Tegtmeyer on flickr) 

Amy March (Amy March on flickr) 

"Patrick" (LordKhan on flickr) 

"Sarah G" (SpooSpa on flickr) 

Igor Maminta (igorms on flickr) 

(mwyllie on flickr) 

"Polo" (pulpolux !!! on flickr) 

Alexis Stapovic (Alexis Stapovic on flickr) 

Sandra Nahdi (Concentrated Passion on flickr) 

(Cristina _sanza on flickr) 

"Aure" (Versatile Aure on flickr) 

Caleb Sconosciuto ('SeraphimC on flickr) 

Jesper Sachmann (Sachmanns.dk on flickr) 

Mareen Fischinger (Mareen Fischinger on flickr) 

"W2 Beard & Shorty" (W2 a-w-f-i-l on flickr) 

Roland Lakis (rolands.lakis on flickr) 

"Corey" (Coreu on flickr) 

"Jonas K." (jonas_k on flickr) 

"SSynth" (SSynth on flickr) 

"Carla S." (le jardin public - CS Photo on flickr) 

Simón Pais-Thomas (Simón Pais-Thomas on flickr) 

(* michel clair on flickr) 

(bs70 on flickr) 

"CK, Carl, Carlo, Carlito" (09traveler on flickr) 

allfr3d (allfr3d on flickr) 

Fernando Gregory (ƒreg on flickr) 

(harvey20887 on flickr) 

Michael Verhoef (nettsu on flickr) 

"Thomas" (streetpreacher83 on flickr) 

(Pictr 30D (B2B) on flickr) 

(kevin41890 on flickr) 

(newpn2000 on flickr) 

(ooodit on flickr) 

(Idleeuw on flickr) 

Chandrachoodan Gopalakrishnan (Ravages on flickr) 

yatenkaiouh (yatenkaiouh on flickr) 

Frédéric DUPONT (darkpatator on flickr) 

(corey (a.k.a. ten0fnine) on flickr) 

Felix Dylan (broma on flickr) 

Jiraroj Sheravanichkul (bookazine on flickr) 

shafiu Jameel (shaapay on flickr) 

David Lytle (davitydave on flickr) 

Tairre Christopherson (milomingo on flickr) 

(laurabaabaa on flickr) 

"Emiliano" (loungerie on flickr) 

Adrián Flores (-.:Ferran : WWT's Ambassador to the 

Aztecs:.- on flickr) 

"Ra'anan Niss." (Ronan_tlv on flickr) 

Nolan Peers (Nolan Peers on flickr) 

Jeremy Brooks (Jeremey Brooks on flickr) 

(auer1816 on flickr) 

"Phillip" (Photog*Phillip on flickr) 

Melissa Segal (Melissa Segal on flickr) 

Paul Baxter (Mr.Baxter on flickr) 

"julián" (Broken Piggy Bank on flickr) 

Matt Bucy (aloofdork on flickr) 
FORM NTIA-29 U.S. DEPARTMENT OF COMMERCE 

(4-80) NAT’L. TELECOMMUNICATIONS AND INFORMATION ADMINISTRATION 

BIBLIOGRAPHIC DATA SHEET 

1. PUBLICATION NO. 

TR-09-459 

2. Government Accession No. 3. Recipient’s Accession No. 

5. Publication Date 

Nov. 2008 

4. TITLE AND SUBTITLE 

Relationships Between Intelligibility, Speaker Identification, and the 

Detection of Dramatized Urgency 

6. Performing Organization 

NTIA/ITS.T 

7. AUTHOR(S) 

Andrew A. Catellier and Stephen D. Voran 

9. Project/Task/Work Unit No. 

6513000-320 

8. PERFORMING ORGANIZATION NAME AND ADDRESS 

Institute for Telecommunication Sciences 

National Telecommunications & Information Administration 

U.S. Department of Commerce 

325 Broadway 

Boulder, CO 80305

10. Contract/Grant No. 

11. Sponsoring Organization Name and Address 

National Institute of Standards and Technology/Office of Law 

Enforcement Standards 

100 Bureau Drive, M/S 8102 

Gaithersburg, MD 20899-8102

12. Type of Report and Period 

Covered 

14. SUPPLEMENTARY NOTES 

15. ABSTRACT 

The systems used for public safety speech communications must be intelligible. It is also desirable that 

they transmit secondary information, such as the attributes of a speaker’s voice. This secondary 

information can allow a user to identify the speaker and his or her emotional state. Testing speech 

communications systems for the delivery of intelligible speech is common. Testing for human perception 

of the delivery of this secondary information is less common, though some prior work has been done. 

Building on this prior work, we describe a set of controlled laboratory listening experiments. These 

experiments characterize the relationships between speech intelligibility, speaker identification, and the 

detection of dramatized urgency in a speaker’s voice across a range of simulated speech processing 

conditions. The experiment results indicate that for the speech processing conditions considered here, 

detection of dramatized urgency is the most robust property, speaker identification is less robust, and 

speech intelligibility is the least robust. 

16. Key Words 

human listening tests; intelligible speech; speaker identification; speaker stress detection; speaker urgency 

detection; speech transmission system; subjective speech quality tests 

18. Security Class. (This report) 

Unclassified 

20. Number of pages 

 35 

17. AVAILABILITY STATEMENT 

 F UNLIMITED. 

19. Security Class. (This page) 

Unclassified 

21. Price: 
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NTIA TECHNICAL MEMORANDUM (TM)

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