An Evaluation of a Spoken Language Interface 

by Paula P. Henry, Timothy J. Mermagen, and Tomasz R. Letowski 

ARL-TR-3477 April 2005 

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Army Research Laboratory 

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ARL-TR-3477 April 2005 

An Evaluation of a Spoken Language Interface 

Paula P. Henry, Timothy J. Mermagen, and Tomasz R. Letowski 

Human Research & Engineering Directorate, ARL 

Approved for public release; distribution is unlimited.
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 An Evaluation of a Spoken Language Interface 

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 Paula P. Henry, Timothy J. Mermagen, and Tomasz R. Letowski (all of ARL) 

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14. ABSTRACT 

Speech recognition software has been proposed for inclusion in the Future Force Warrior system. DynaSpeak is a speech 

recognition software product developed by SRI International that has been shown to function well in quiet environments. 

However, the system has not yet been evaluated in noise environments representative of Army operational environments. This 

report summarizes a study that was conducted to evaluate the performance of the DynaSpeak software in the presence of two 

types of noise: steady state noise obtained from a tracked vehicle and impulse noise obtained from gunfire. Simultaneous 

recordings were made from 12 participants wearing two microphones: a Gentex noise-canceling boom microphone and a 

Temco HG-17 bone conduction microphone. The 12 participants verbalized call signs and commands which were analyzed by 

the speech recognition software. The results of the present study show that the DynaSpeak software functions well through the 

boom microphone in moderately high steady state noise (90 decibels A-weighted [dBA]) with error rates for words of 2% to 

5%. However, the performance of the system with the same boom microphone in high steady state noise levels (110 dBA) or 

in moderately high impulse noise levels (90 dBA) does not meet the requirements for military applications. Error rates for 

words in these conditions ranged from 14% to 40%. If the DynaSpeak software is to be used in environments of impulse noise 

or high levels of steady state noise, the performance of the system must be enhanced. 

15. SUBJECT TERMS

 bone conduction; speech recognition 

16. SECURITY CLASSIFICATION OF: 

19a. NAME OF RESPONSIBLE PERSON

 Paula Henry 

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iii 

Contents 

List of Figures iv

List of Tables iv

1. Introduction 1

2. Methods 1

3. Results and Discussion 8

3.1 Results From the DynaSpeak System .............................................................................8 

3.2 Results From Listeners With Normal Hearing..............................................................12 

4. Conclusions 14

5. References 15

Appendix A. Commands and Call Signs Used as Speech Stimuli 17

Appendix B. Results From the DynaSpeak Software for Individual Talkers 21

Distribution List 22 
iv 

List of Figures 

Figure 1. Photographs of the two microphone systems used in the present study..........................4 

Figure 2. Results from the DynaSpeak software on the recordings made through the boom 

microphone, averaged across listeners and showing the percentage of errors of words 

from each condition ...................................................................................................................8 

Figure 3. Results from the DynaSpeak software on the recordings made through the bone 

conduction microphone, averaged across listeners and showing the percentage of errors 

of words from each condition ....................................................................................................9 

Figure 4. Average results for the 12 participants who completed the listening task com-pared 

to the results from the DynaSpeak software for the talker who resulted in the software’s 

worst performance ...................................................................................................................13 

List of Tables 

Table B-1. Results from the DynaSpeak software for individual talkers. (Recordings were 

made from the Gentex noise-canceling boom microphone.)...................................................21 

Table B-2. Results from the DynaSpeak software for individual talkers. (Recordings were 

made from the Temco HG-17 bone conduction microphone.)................................................21 
1 

1. Introduction 

The Future Force Warrior (FFW) program is considering the inclusion of a spoken language 

interface (also known as speech recognition software) for control of FFW system functions, 

including serving as the controller module for remote control of unmanned vehicles. One such 

product is the DynaSpeak1 software developed by SRI2 International. This system has been 

shown to function well in quiet environments but has not yet been evaluated in the presence of 

high noise and impulse noise environments representative of Army operational environments. 

The U.S. Army Research Laboratory (ARL) was approached by the Natick Soldier Center 

(NSC), Massachusetts, to conduct such an evaluation for a moving tracked vehicle (steady state 

noise) and gun fire (impulse noise) environments. This report summarizes the results of the 

evaluation. 

The purpose of the study was to evaluate the effectiveness of the SRI DynaSpeak spoken 

language interface in high noise and impulse noise environments. This study sought to determine how well the SRI DynaSpeak software would recognize speech during a limited set of 

adverse operational conditions. It also sought to provide an answer as to whether the software 

performs more poorly, just as well as, or better than listeners with normal hearing in the same 

specified environments. This planned comparison was in case the DynaSpeak software did not 

perform well. In this case, a comparison to performance of listeners with normal hearing would 

provide information about whether the software or the test was the cause of the failure. The SRI 

DynaSpeak software, which meets the Army speech intelligibility criterion of 91% (Department 

of Defense, 1989), has been demonstrated to work well in quiet environments with various 

talkers. Through the use of noise-reduction algorithms, the software was expected to function 

well in steady state high intensity background noise generated inside and outside a moving 

vehicle. The function of the system in impulse noise environments was not projected to be 

acceptable for outgoing fire. However, such an acoustic environment may be prohibitively loud 

for direct speech communication. Therefore, the performance of the DynaSpeak software was 

compared with the recognition scores obtained from human listeners with normal hearing for 

data normalization. 

2. Methods 

Twelve adults (10 civilian and 2 military) with normal hearing sensitivity were recruited to serve 

as talkers and listeners in the study. Participants were between the ages of 18 and 51 years old 

 1

DynaSpeak is a trademark of SRI International. 

2Formerly Stanford Research Institute 
2 

(mean = 33). All participants had normal hearing defined as thresholds of 20 dB hearing level 

(HL) or better in both ears across 500 to 8 kHz (ANSI, 1996). The group of participants 

consisted of 10 men and 2 women. 

The study had two portions: recording and listening. The recording portion was completed by 

all the participants before the listening portion was conducted. The recording portion of the 

study was conducted in the hostile environment simulator (HES) of building 518 at Aberdeen 

Proving Ground (APG), Maryland. The HES is a large (17.4 m long by 13.4 m wide and 6.7 m 

high) hall that has relatively low ambient noise levels measuring less than 30 dB A-weighted3

(dBA). The measured reverberation time varies, depending slightly on the location of the 

microphone within the space, but is approximately 0.5 second in the frequency range from 500 

Hz to 4000 Hz. 

The listening portion took place in two sound-treated test booths in building 520, room 30, at 

APG. Two identical listening stations were set up with as many as two listeners at each station. 

This allowed for data collection from as many as four listeners at any given time. 

At the beginning of the recording session, the participant was briefed about the research task and 

was asked to read and sign a volunteer agreement affidavit. The participant was then seated in 

the center of the HES and asked to verbalize lists of items. These lists included items from the 

Call Sign Acquisition Test (CAT) and simple commands used to operate unmanned vehicles 

(appendix A). The CAT consists of all 126 possible combinations of the 18 two-syllable Army 

phonetic alphabet and the seven one-syllable numbers from 1 through 8 (9 is not used because it 

is pronounced “niner” in radio communications); e.g. alpha 3, kilo 6 (Rao & Letowski, 2003; 

Rao, Letowski, & Blue, 2002). The list of commands was provided by SRI International and 

approved by representatives from MicroAnalysis & Design and NSC, who took part in the 

design of the study. 

Participants were seated at a desk in the center of the HES, which contained two computer 

screens, a keyboard, and the two headsets to be used in the study. Before the participants were 

fitted with the headsets or were instructed in the use of the computer interfaces, they were 

presented with a list of the commands that would be used in the study. The participants were 

told that these were potential military-relevant commands that could be used with speech 

recognition software. They were asked to read aloud the list of 125 commands so that the 

experimenters could be assured that the participants were comfortable with how to say each of 

them and to answer any questions. The participants were also familiarized with the call signs. 

Variations in pronunciation were permitted for both the commands and the call signs (i.e., 

“route” in the command “route off” could sound like “rout” as in router or like “root”; Quebec 

in the call signs could be pronounced with a “qw” sound or a “k” sound at the beginning). 

 3

A-weighted sound level measurements refer to measurements in which the sound levels of each frequency band 

are weighted in order to accommodate frequency-dependent changes in human auditory sensitivity at low intensity 

levels. 
3 

After the experimenter felt the participant was comfortable reading the call signs and the 

commands, the participant was fitted with hearing protection and the two headsets that would be 

used for recording. E•A•R4 foam earplugs (noise reduction rating [NRR] = 30) and the combat 

vehicle crewmen (CVC) ear cups were used together to form an effective double hearing 

protection system. 

Two microphones were used for making simultaneous recordings: a Temco HG-17 bone conduction microphone and a noise-canceling Gentex boom microphone extracted from a CVC 

helmet. The simultaneous use of both microphones was critical (identical verbalization of each 

phrase) for enabling meaningful comparisons of data obtained with each of the microphones. 

Figure 1 shows the two headsets that were used together in the present study. The HG-17 is a 

self-contained bone conduction communication system with a bone conduction microphone and 

bone conduction vibrators. This is a commercially available system not intended for acoustic 

environments. No modifications were made in the headset. The HG-17 bone conduction 

microphone was first placed on top of the talker’s head. Participants were asked to adjust the 

headset so that the bone conduction microphone made good contact with their skulls. A helmet 

was not used in the study in order to ensure good contact between the bone microphone and the 

talker’s head. After the bone microphone was placed on the talker’s head, the position of the 

microphone and the static force pressing the microphone against the talker’s head were adjusted 

to maximize the microphone output while ensuring the talker’s comfort. If a helmet had been 

used, sufficiently good contact could not have been ensured for all the talkers. Such a bare head 

arrangement compromised low-frequency noise reduction by the bone microphone (through the 

absence of microphone shielding provided by a helmet). In the speech range (500 to 4000 Hz), 

natural reduction of external noise by the bone microphone is much higher than at low 

frequencies and the shielding action of a helmet is not as critical. Since the signal-processing 

algorithm in the DynaSpeak software is not affected by the upward spread of masking caused by 

low-frequency noise, the lack of noise shielding provided by the helmet should not affect the 

performance of the DynaSpeak software, but this lack of noise shielding could affect to some 

degree the live listener’s performance. Therefore, it was determined that good contact between 

the bone microphone and the talker’s head was more critical to the study than noise shielding of 

the bone microphone through use of a helmet. 

The boom microphone in the CVC helmet includes wiring that is incorporated into the ear cups 

of the helmet. Since it was not desirable to use a helmet in the study, the boom microphone 

needed to be separated from the CVC helmet. The ear cups and boom microphone were 

extracted from the helmet and mounted into an earphone headband (see figure 1). This allowed 

the participant to wear a set of headphones with the boom microphone attached. Once the talker 

had the HG-17 headset in place, the boom microphone and headband were placed in such a 

manner as not to have the headband pressing directly on the bone conduction microphone. The 

use of the two systems together allowed for simultaneous recordings from both microphones. 

 4E•A•R is a registered trademark of Cabot Safety Corporation. 
4 

The typical placement of the headband from the earphones was just above the talker’s forehead. 

The location of the boom microphone was adjusted so that the microphone tip was centered on 

and 0.25 inch from the talker’s lips (Department of the Army, 1997). 

 

Figure 1. Photographs of the two microphone systems used in the present study. (The left photograph is 

of the Temco HG-17 bone conduction system and the right photograph is of the boom microphone and ear cups from the CVC helmet mounted into a separate headband. No other 

headgear was used in the study.) 

The Temco HG-17 communication system also includes a push-to-talk system (BM8). This 

device must be depressed to activate the bone conduction microphone. Once the microphone is 

activated, it stays on until the talker presses the button a second time or the talk circuit times out. 

The talkers were instructed in the use of the push-to-talk system and were instructed to reactivate 

the microphone after verbalizing approximately 25 items. The investigator sitting next to the 

talker confirmed that the microphone was always active during the recording periods. 

The signal output levels from both microphones were set at the input to the computers to provide 

a maximum undistorted (no clipping) signal at the DynaSpeak input. The levels were set based 

on one of the experimenter’s voices and maintained for all subsequent talkers. This procedure 

resulted in uniform undistorted recording levels for all the talkers and the bone microphone. In 

the case of the boom microphone, the recording level was set too high for the first two 

participants, resulting in excessive clipping. Therefore, these recordings were not used in the 

subsequent analysis of the data, and the input level for the boom microphone was slightly 

lowered for the rest of the talkers. 

As stated before, the desk at which the talkers sat held monitors connected to two personal 

computers. These monitors displayed the DynaSpeak communication screens to the talker. The 

recordings were made from the two microphones simultaneously, one fed to each computer’s 

sound card. This enabled two identical verbalizations to be analyzed through each of the two 

microphones. Two separate versions of the DynaSpeak software were used: one with a noise 

reduction algorithm designed for the bone microphone and one designed for the boom micro-
5 

phone. Each microphone output was “hard wired” to the appropriate computer and the computers 

were labeled so that the participant knew which screen corresponded to which microphone. 

The DynaSpeak software prompts the talker for each item (commands or call signs) that the talker 

is asked to record. The software also includes a volume unit meter that allows the talker to 

monitor the signal being received by the computer from the microphone. This proved especially 

useful in helping us determine whether the bone conduction microphone was transmitting a 

signal. Additionally, the software provided feedback to the talker as to what the DynaSpeak 

software determined the spoken phrase was, and it kept a running total of percent of words and 

sentences that were recognized correctly. The provision of feedback in speech recognition 

software allows the talker to repeat phrases or adjust his or her pronunciation to improve the 

system’s ability to recognize the talker’s voice. In the current evaluation, it was not desirable to 

have the talkers adjust their voices in any way. Therefore, the parts of the screen that displayed 

feedback and the running score were covered with paper to hide them from the talkers’ view. 

A wireless keyboard and two wireless receivers were used to synchronize the recordings on the 

two computers. The participant recorded a command or call sign by holding the control key 

(CTRL) on the keyboard and then vocalizing the phrase. When the talker released the control 

button and hit the “next” button (right arrow key), the software analyzed the recorded phrase and 

continued to the next item. The talker was instructed to speak with such effort as s/he normally 

would to communicate in the noise environment in which s/he was placed. The talker was also 

instructed to only repeat if s/he misspoke. For instance, if the command was “route off” and the 

talker said “route on,” the item could be re-recorded in the same way it was initially before the 

next key was hit. 

The recordings were analyzed by the DynaSpeak software on each system in real time as they 

were being recorded, and a cumulative percent recognition score for each system was maintained. The DynaSpeak software created the *.wav files with the recorded voice segment and a 

text file with the analysis of the performance of the verbalization. On rare occasion, the two 

computers were not synchronized. This did not occur very often, but when it did, the assisting 

experimenter stopped the participant and reversed the DynaSpeak software a number of items to 

re-synchronize the computers. At this point, the participant was asked to re-record the first item 

where the computers were noted by the experimenters or the participant to be out of synchronization, and the experiment was continued. 

Two noises were selected for use in the evaluation: steady state noise recorded from a tracked 

military vehicle and military-relevant impulse noise (gunfire). They were representative 

examples of the steady state noise and the impulse noise present in military environments. The 

use of these two noises was requested by the FFW office, based on their military relevance and 

their representation of noises common to infantry Soldiers. The steady state noise was created 

from a digital recording of an M1A2 tank moving at approximately 10 mph on a flat gravel track. 

The noise was recorded through a microphone mounted outside the tank. The impulse noise was 
6 

a creation of multiple weapons fire relatively close to the listener. The weapons fire was taken 

from a battle scene in a movie with added simulated machine gun fire. 

Two presentation levels were selected for each noise in the study: 90 and 110 dBA. These two 

noise levels represent lower and upper boundaries of predominant battlefield noises. The 90-dBA 

level is typical for acoustic environments in and around idling tracked vehicles. It is also a 

characteristic noise level for a person on the battlefield not being involved in direct action 

(receiving and returning direct fire). The level of 110 dBA represents the level common inside 

and in proximity to rapidly moving tracked vehicles. It is also a reasonable estimate of the 

average noise exposure level of a person involved in an exchange of direct fire. In other words, 

the levels of 90 and 110 dBA represent moderate and high level noise exposure on the battlefield, 

respectively. 

Participants completed a total of 16 recordings (2 microphones x 2 noise conditions [impulse 

noise, steady state tank noise] x 2 noise levels [90 and 110 dBA] x 2 sets of stimuli [call signs, 

commands]). The ordering of the recordings was based on a Latin square design where three 

participants followed any given order. The ordering of items within a block was randomized 

before the study began, and the same randomization was used for each of the participants. Both 

microphone recordings were obtained simultaneously for each talker, resulting in eight total 

blocks of recordings per talker. The two sets of stimuli (call signs and commands) were 

presented in separate blocks to allow for analysis to be conducted on each set of stimuli 

separately. Both recordings were played back in the test environment (HES) at two different 

levels: 90 and 110 dBA. For the impulse noise recording, the presentation level was set, based 

on the machine gun noise peaks in the noise file. The participants were required to wear E•A•R 

foam plugs in addition to the ear cups from the CVC helmet in order to ensure adequate 

protection for hearing. The foam plugs were inserted into the participants’ ears by the participant 

with experimenter supervision or by the experimenter, depending on participant’s preference as 

well as the experimenter’s determination of a proper fit. 

Because the HG-17 communication system and the ear cups were used simultaneously, an 

adequate seal between the ear cups and the listener’s head was not obtained. Therefore, we 

elected to use a double hearing protection scheme with the E•A•R foam plugs serving as primary 

hearing protectors and providing approximately 15 to 30 dB of hearing protection and the ear 

cups serving as secondary hearing protectors. 

During recordings, the participants were provided several breaks in a quiet environment to rest 

their voice and hearing. Calculations of total noise exposure were made to ensure a safe 

recording environment for the participants. All participants completed the recording sessions 

before any completion of the listening sessions. 

Once the recordings were complete, the results of the DynaSpeak software were evaluated. The 

evaluation focused on the number of words and sentences that the DynaSpeak software did not 

correctly recognize. Separate values were obtained for the commands and call signs. The 
7 

conditions in which the recordings were made were documented and sound files were organized 

for subsequent playback to the participants who served as human listeners. The selection of the 

recordings to be used for the listening portion of the study represented the worst case scenario. 

First, the talker whose recordings resulted in the greatest number of errors by the DynaSpeak 

software was chosen. Next, three conditions for the boom microphone were chosen for presentation to the listeners. These three conditions represented recordings in which the software 

performed poorly. Knowing that the human listeners should outperform the DynaSpeak 

software, this was the best scenario for comparing relative effects of the test conditions. 

For the human listener evaluation, the recordings obtained from the talker in any given condition 

were combined into a single sound file. We accomplished this by taking all the *.wav files and 

adding a sample of the background noise from that recording between items. We obtained the 

samples of background noise by making noise-only recordings through each of the microphones 

in each of the noise conditions. The length of the background noise sample placed between the 

items varied, based on the type of stimuli. For the CAT items, the background noise sample 

lasted 2 seconds. For the commands, the background noise sample lasted 10 seconds. The 

ordering of the items was randomized via a computer program designed for this purpose. The 

same randomization was used for all participants. 

The listening portion of the study was performed in ARL’s sound-treated booths in room 30 in 

building 520 at APG. Two separate listening stations were established. Personal computers 

were used to play the sound files through SoundForge5 software. The output of the sound cards 

was first routed to a Rane HC6 headphone amplifier and then to two pairs of AKG (Acoustics 

GmbH) K240 DF6 studio monitor headphones. Individual volume controls were provided for 

each listener so that s/he could adjust the output to the headphones to a comfortable listening 

level. Each participant listened to recordings made in three conditions: the CAT recorded in 110 

dBA of steady state tank noise, the CAT recorded in 90 dBA of impulse noise, and the 

commands recorded in 110 dBA of tank noise. The participants were told what they were going 

to hear and how they were going to record their responses. 

If the recording was of the CAT call signs, the listeners were instructed to identify each phonetic 

alphabet letter and number combination (call sign) by writing the letter followed by the number. 

For example, if s/he heard “alpha six,” s/he should record “A6.” If the recording was of the 

command phrases, the listeners were instructed to write the whole phrase. The listener was 

encouraged but not required to abbreviate some of the words to save time. A new record sheet 

was used for each condition, and each record was coded with condition and participant numbers. 

The ordering of the recordings played to the listeners followed a Latin square design where every 

four listeners followed a different order. 

Twelve participants completed the listening portion of the study. All the listeners completed the 

recording portion of the study before they completed the listening portion. The record sheets 

were scored on the basis of percent of items (call signs or commands) recognized correctly. The 

 5

SoundForge is a trademark of Sonic Foundry. 

6not an acronym 
8 

performance of the participants in the listening conditions was then compared to that of the 

DynaSpeak software. 

3. Results and Discussion

3.1 Results From the DynaSpeak System 

The items read by each talker were analyzed by the DynaSpeak system in real time as the 

recordings were being made. After recordings of any given talker were made, the cumulative 

percentage of errors was extracted from the data log files contained in each talker’s folder for 

each of the microphone, stimulus set, noise, and intensity level combinations. Word level and 

sentence (phrase) level errors were calculated. As per the recommendation from the FFW office, 

presented discussion of the data is focused on the percentage of words that were not recognized 

correctly (percent of errors). 

The percentage of word errors for each of the conditions after being analyzed by the DynaSpeak 

software is shown in figures 2 and 3. Figure 2 shows the results for the boom microphone, and 

figure 3 shows the results for the bone conduction microphone. Three general and consistent 

patterns can be seen in these figures. First, the results demonstrate that the software functioned 

better at the lower noise level (90 dBA) than at the higher noise level (110 dBA). This is the 

expected result, but the important characteristic is the size of the difference. 

0

20

40

60

80

100

90 110

Noise Level (dBA)

Word Errors (%)

CAT items, tank

noise

Commands, tank

noise

CAT items, impulse

noise

Commands,

impulse noise

Figure 2. Results from the DynaSpeak software on the recordings made through 

the boom microphone, averaged across listeners and showing the 

percentage of errors of words from each condition. (Error bars indicate 

+1 standard deviation.) 
9 

0

20

40

60

80

100

90 110

Noise Level (dBA)

Word Errors (%)

CAT items, tank

noise

Commands, tank

noise

CAT items,

impulse noise

Commands,

impulse noise

Figure 3. Results from the DynaSpeak software on the recordings made through 

the bone conduction microphone, averaged across listeners and showing 

the percentage of errors of words from each condition. (The error bars 

indicate +1 standard deviation.) 

Second, small but consistent differences appear between the two types of noises (tank and 

impulse). The software made fewer errors in speech recognition with the relatively steady state 

tank background noise than with the impulse noise. This is probably attributable to the noise 

reduction algorithm’s ability to sample and more effectively cancel a noise that is steady state in 

nature. It is quite possible that further fine tuning of the processing algorithm may result in 

indifference to the type of noise. 

Third, the software functioned better for the stimuli that consisted of commands rather than call 

signs. This is probably because of the presence of contextual effects in commands that are not 

present in call signs. The software uses a limited set vocabulary and is testing for the presence of 

specific words and word combinations. The probability of correct guessing of the CAT phrase is 

less than 1%, assuming that the DynaSpeak system expects only a CAT phrase and much less 

than 1% if not. Conversely, in the commands set, there are some unique phrases, in which after 

the leading word is recognized, there is only one option for the next word. 

The difference in performance between the two noise levels, 90 and 110 dBA, is most likely 

attributable to differences in signal-to-noise ratio (SNR) at the input to the microphone. Recall 

that the talkers were instructed to speak as they would in real life in order to communicate in 

these specific noise environments. It is natural for a talker to raise his or her voice in the 

presence of noise in order for his or her speech to be understood by a listener. The talker would 

raise his or her voice in both noise conditions; however, the resultant SNR may differ. The 

projected lower SNR provided to the system in the 110-dBA condition would explain to some 
10 

degree the difference in error rates between the two noise levels. SNRs cannot be accurately 

measured directly because the speech levels are less than the levels of the background noise, thus 

resulting in a negative SNR. For several of the talkers, an indirect measurement was made of 

their vocal effort in the presence of 110 dBA noise. We measured this by playing the noise and 

then turning it off abruptly when the talker was speaking. The intensity levels of these talkers at 

the location of the boom microphone were between 85 and 95 dBA. 

Please note that talkers are unable to maintain a normal speech level (vocal effort) in noise even 

if asked to do so. This is called the “Lombard effect”. The voice level unconditionally increases 

with the increase in noise level, especially for noise levels exceeding 70 dBA. This increase is 

accompanied by a slower rate of speech, improved pronunciation, and changes in the spectral 

envelope of speech. All these changes result in Lombard speech being much easier to understand 

than normal speech by the listeners with normal hearing and by automatic speech recognition 

systems (Chen, 1988; Chi & Oh, 1996; Junqua, 1989; Letowski, Frank & Caravella, 1993; 

Summer, Pisoni, Bernacki, Pedlow, & Stokes, 1988). This improvement in speech recognition 

can be as large as 15% for a noise level of 90 dBA and an SNR = 10 dB (Chi & Oh, 1996). 

Therefore, the increase in noise level accompanied by Lombard speech may result in poorer SNR 

but not necessarily poorer speech intelligibility. This is obviously not true for high noise levels in 

which the level of Lombard speech has reached its limits because of natural limitations of the 

human vocal system. It is believed that the critical noise level is about 90 to 100 dBA, depending 

on the type of noise. 

The differences seen between the two noise conditions (tank and impulse) are most likely 

attributable to the difference in the ability of the noise-reduction algorithms to eliminate noises 

of different types. For a relatively steady state noise, a sample of the noise can be taken and an 

algorithm created to minimize its impact on the speech signal. The noise recorded from a tank 

does not change significantly over time. Therefore, at any given moment, the background noise is 

relatively the same and remains so across different speech items. This is not necessarily true with 

impulse noise. In this case, the noise varies in time and is less predictable in nature. Therefore, 

an algorithm to reduce the impact of impulse noise needs to sample the background noise much 

more frequently and does not necessarily improve its performance in time because of constant 

changes in the nature of the noise. In addition, the presence of a speech-like background mixed 

into the impulse noise could to some degree confuse the DynaSpeak recognizer. It is unlikely that 

this was a major factor in the present study because the speech levels were 30 or more decibels 

below the level of the impulse noise. However, the human voice and speech “babble” need to be 

used as interfering noises in future studies to ensure the DynaSpeak’s effectiveness in such 

environments. 

The differences seen between the two sets of stimuli (call signs and commands) are probably 

attributable to their differences in contextual cues. Speech recognition systems often operate by 

comparing individual words against a list of potential phrases containing that particular word. 

The number of words that the software can recognize is often referred to as the software’s 
11 

vocabulary, and the rules that bind individual words in phrases (choices) as the software’s 

grammar. The size of the vocabulary for any particular group of phrases for the DynaSpeak 

system is not known, but it is reasonable to assume that the probability of occurrence of a 

specific combination of words is smaller for commands than for call signs. In the set of 

commands provided for the study, some words appeared in only a small number of phrases. For 

example, the word “cad” only correlated to one phrase, “cad_r_g”. In this case, the number of 

potential phrases containing the word “cad” was one. So, as long as the word “cad” was 

recognized correctly, the entire phrase should also be recognized correctly. On the other hand, 

for some words, the number of possible phrases containing that particular word could be quite 

large. For instance, the word “change” was the first word in 12 different phrases, so the 

recognition of this particular word did not ensure the software’s ability to recognize the entire 

command correctly. Therefore, the chance that the software will recognize a complete command 

correctly varies by individual command set. 

For the call signs, any given alpha item could be paired with any given number, and the chances 

of the software recognizing the entire call sign correctly (both alpha and numeric) were the same 

for each call sign. Recall that there were 18 alpha and 7 numeric items in the set that the talker 

read, and they could be presented in any alpha-number combination. Therefore, the CAT tested 

the performance of the DynaSpeak system, based on an equal and low probability of each item. 

The probability was further decreased because the DynaSpeak vocabulary for call signs included 

all 26 alpha and 10 numeric items. The call sign test condition was probably very similar to that 

created by the set of command phrases using the word “change” as the initial word. However, 

overall, the choices the DynaSpeak system had to make for the command set were based on 

fewer possibilities than those produced by the CAT items because of unequal and sometimes 

high probability of an item’s occurrence. Therefore, any change in the number and type of 

phrases used in the command set would likely affect performance of the system even if all the 

individual words were in the DynaSpeak vocabulary. Conversely, replacing specific items in the 

CAT set should not affect the DynaSpeak performance as long as these items are in the 

DynaSpeak vocabulary. Thus, the score for the CAT may be considered as an indicator of 

DynaSpeak’s average ability to recognize individual words from its vocabulary, whereas the 

score for the command set may be considered as an indicator of how the pre-selected set of items 

maximizes current DynaSpeak capabilities. 

The use of two microphones allowed for an evaluation of how the software would perform with 

differences in input SNR, noise-canceling options, and differences in available frequency 

bandwidth of the talker’s voice. A comparison of figures 2 and 3 reveals that the boom 

microphone input was processed better by the DynaSpeak system than by the bone microphone 

input. This can be attributable to several factors: (1) the bone microphone program was not 

processing signals equally well as the boom microphone program, (2) the bone microphone 

provided a much poorer signal than the boom microphone and this difference could not be 

compensated by software adjustments, (3) a lack of noise shielding for the bone microphone 
12 

affected its performance, (4) the sound cards in both computers differed in their performance, 

and (5) the placements of the boom and bone microphones differed in their optimization. All the 

factors listed could simultaneously affect the data; however, the greatest contributor seems to be 

item (2). Noise-canceling microphones are mature technology and the Gentex noise-canceling 

microphone used in this study is recognized as one of the best on the market. Conversely, the 

bone microphone technology is just entering the market and cannot be described as a mature 

technology. The Temco microphone was the only reasonable choice several months ago, but it 

has growing competition. Therefore, the presented data can only be considered as the data 

describing performance of two specific transducers but cannot be generalized to all “boom 

microphones” and “bone microphones” as categories of transducers. 

As stated earlier, a decision had to be made as to which aspect of experiment instrumentation 

was more critical to the current study: (1) maintaining good contact between the bone microphone and the talker’s skull or (2) shielding the bone microphone through use of a helmet. 

Although the negative effect of lack of shielding on the bone microphone data cannot be totally 

discarded, the data presented in figures 1 and 2 show clearly that the repeatable type of contact 

between the bone microphone and the talker’s skull has been ensured. Data variabilities 

obtained for the boom and bone microphones during various test conditions are very similar, thus 

indicating similar repeatability. The interfacing of both types of microphones can therefore be 

considered as equally reliable. 

3.2 Results From Listeners With Normal Hearing 

Individuals with normal hearing sensitivity were recruited to listen to several of the recordings 

and provide speech recognition data. This additional task was conducted in order to compare the 

software’s performance with what would be expected by humans in the same environment. The 

results of an evaluation of speech recognition software without a comparison to what would be 

expected from listeners with normal hearing operating in the same acoustic environment could 

not be labeled as good, fair, or poor since there would be no basis of comparison. 

It is well known that individuals with normal hearing can recognize speech at a negative SNR, 

which indicates that the intensity of the speech is less than that of the noise. Furthermore, speech 

recognition systems typically require a positive SNR for optimal performance. However, given 

the use of noise-reduction algorithms, noise-canceling microphones, and two microphones with 

different spectral content, it is unknown to what extent the performance of the software would 

agree with that of listeners with normal hearing. 

The percentage of errors obtained from the DynaSpeak software for each individual talker is 

presented in appendix B. Three boom microphone recordings of a single voice presenting the 

worst case scenario were selected for human subject evaluation. These recordings were the CAT 

recorded in 110 dBA of tank noise, the CAT recorded in 90 dBA of impulse noise, and the 

commands recorded in 110 dBA of tank noise. The reason for selecting the worst case scenario 

for human subject listening was twofold: (1) there was relatively good overall performance of 
13 

the DynaSpeak system with the boom microphone input with a 90-dBA noise level and (2) there 

was a need to determine how poor the DynaSpeak performance can be in comparison to human 

hearing performance when there is a mismatch between the DynaSpeak software and human 

voice. Please note that if conditions when the DynaSpeak software performed quite well were 

selected, any higher performance by listeners with normal hearing would not be of much interest. 

Figure 4 shows the average results from the listening tasks, along with the performance of the 

DynaSpeak system for the same three recordings for comparison. 

As seen in figure 4, the listeners performed far better than the DynaSpeak software with the 

talker whose voice resulted in the poorest performance of the software, as shown by lower 

percentages of errors. As stated previously, the fact that listeners with normal hearing generally 

function better than a speech recognition system is not in itself surprising as this has been 

demonstrated previously. What is of interest is the degree to which the two differed in this 

particular evaluation. For the three recordings that were chosen, the difference in performance 

was on the order of 30% to 40%. This shows that the software was substantially worse than 

what would be expected from a listener with normal hearing. However, the performance of the 

listeners with normal hearing was not acceptable, which indicated that although the system’s 

performance should be improved, one cannot expect it to perform better than an error rate of 

30% to 60% in 110 dBA noise (sentences) for some type of voices. 

31.75

88.89

64.79

1.00

56.00

27.00

0

20

40

60

80

100

90 CAT

impulse

110 CAT

tank

110

impulse

commands

Word Error (%)

DynaSpeak Results

Listener Results

Figure 4. Average results for the 12 participants who completed the listening task compared to the results from the DynaSpeak software for the talker who resulted 

in the software’s worst performance. (All results are from recordings made 

through the boom microphone. Error bars indicate +1 standard deviation.) 
14 

4. Conclusions 

The DynaSpeak system by SRI International has been demonstrated previously by the developers 

to function well in quiet environments. The results of the present study show that the system 

also functions commendably well in moderately high steady state noise levels (90 dBA). 

However, the performance of the system in high noise levels (110 dBA) or in moderately high 

impulse noise levels (90 dBA) does not meet the requirements for military applications. If the 

DynaSpeak software is to be used in these conditions, the performance of the system must be 

enhanced. Otherwise, the software application should be limited to no more than 90 dBA of 

steady state noise. Finally, the software needs to be tested in a background noise consisting of 

human voices to determine its robustness in this type of environment. 
15 

5. References 

American National Standards Institute. Specification for Audio Meters; ANSI 3.6-1996; New 

York, 1996. 

Army Hearing Conservation Policy. Amendment to DA PAM 40-501, Washington, D.C., 1994. 

Chen, Y. Cepstral domain talker stress compensation for robust speech recognition. IEEE 

Trans. ASSP 1988, 36, 433-439.

Chi, S.; Oh, Y. Lombard effect compensation and noise suppression for noisy Lombard speech 

recognition. Proceedings of the Fourth International Conference on Spoken Language 

Processing (CDROM, vol. 4, Paper #065), Philadelphia, PA, 3-6 October 1996. 

Department of the Army. Technical Manual: Operator’s Manual: Intercommunication Set, 

Vehicular AN/VIC-3(V), TM 11-5830-263-10, Washington, D.C., 1997. 

Department of Defense. Military Standard: Human engineering design criteria for military 

systems, equipment and facilities. MIL-STD-1472D, Washington, D.C., 1989. 

Junqua, J. C. The Lombard reflex and its role on human listeners and automatic speech 

recognizers. Journal of the Acoustical Society of America 1989, 85 (2), 849-900. 

Letowski, T.; Frank, T.; Caravella, J. Acoustical properties of speech produced in noise 

presented through supra-aural earphones. Ear and Hearing 1993, 14, 332-338. 

Rao, M. D.; Letowski, T. R. Speech intelligibility of the Call Sign Acquisition Test (CAT) for 

Army communication systems. Audio Engineering Society Convention Paper 5835. 

Presented at the 114th Convention 2003, May 22-25, Amsterdam, The Netherlands, 2003. 

Rao, M. D.; Letowski, T. R.; Blue, M. Subjective-objective assessment of speech intelligibility 

for the Callsign Acquisition Test (CAT). Unpublished data analysis, 2002. 

Summer, W. V.; Pisoni, D. B.; Bernacki, R. H.; Pedlow, R. I.; Stokes, M. A. Effects of noise on 

speech production: acoustic and perceptual analyses. Journal of the Acoustical Society of 

America 1988, 93 (1), 917-928. 
16 

INTENTIONALLY LEFT BLANK
17 

Appendix A. Commands and Call Signs Used as Speech Stimuli 

A1. Commands: 

change one down to c_two reporting display 

v_map one 

observation point 

linkup point 

action taken observing 

change two up to tactical situation display 

points off 

change one up to situational awareness sensor 

display 

zoom fifty percent 

shelter surface 

done 

mortar wheeled 

way point 

action taken request resupply 

vehicle up on 

zoom to oh point twenty five 

zoom to one 

air cavalry 

change three down to rsta display 

enemy activity attacking 

signal 

battle position section 

change one up to system status display 

squad 

communications 

release point 

friendly neutral on 

objective area 

infantry 

motorized infantry 

battalion 

cancel 

coordination point 

regiment 

named area of interest 

zoom to three point oh 

zoom to two point zero 

wheeled mortar 

passage point 

enemy activity engaging 

cancel 

o_n_c 

spot report 

change three down to target acquisition display 

platoon 

action withdrawing 

enemy activity request resupply 

engagement area 

change one down to w_c_a alert list display 

route off 

undo last command 

cad_r_g 

annotation on 

enemy activity river crossing 

action taken reconnaissance 

mortar 

aircraft generic 

edit type 

way point 

add to target queue 

unit 

team 

areas on 

infantry motorized 

declutter 

movement one hundred degrees 

done 

underground shelter 

friendly off 

map overlap on 

generic aircraft 

engineer 

down 

battle position platoon 

targeted area 

enemy unknown off 

zoom two hundred percent 

change three down to c_two reporting display 

n_b_c smoke 

change one up to s_a sensor display 

friendly 

zoom to zero point two five 

n_b_c unknown 

enemy 
18 

map on 

done 

enemy on 

change two down to unmanned asset control 

display 

cancel 

zoom three hundred percent 

company 

unknown 

medical 

cursor center off 

action acquire 

view 

zoom out 

done 

change two up to unmanned asset control 

display 

enemy nationality friendly 

mechanized infantry 

maintenance 

v_p_f 

check point 

headquarters 

n_b_c area biological 

change one down to s_a sensor display 

recon 

zoom to three point zero 

target reference point 

field artillery 

reconnaissance 

passage point 

pan right 

start point 

enemy activity guarding 

change three up to r_s_t_a display 

enemy activity defending 

critical point 

load 

general point 

pan left 

done 

zoom to one point five 

n_b_c area unspecified 
19 

A2. Call signs: 

quebec four 

oscar two 

hotel three 

whiskey three 

bravo eight 

hotel five 

hotel four 

whiskey four 

x-ray six 

charlie four 

lima four 

yankee three 

victor five 

victor six 

papa two 

tango two 

quebec five 

quebec eight 

yankee five 

bravo five 

foxtrot two 

whiskey two 

foxtrot five 

Kilo eight 

hotel one 

hotel six 

tango three 

foxtrot one 

lima two 

papa four 

x-ray eight 

charlie one 

bravo two 

echo four 

quebec six 

bravo six 

foxtrot six 

foxtrot eight 

oscar three 

kilo two 

lima three 

delta one 

oscar four 

delta four 

alpha four 

tango one 

whiskey six 

foxtrot four 

quebec three 

victor two 

tango four 

yankee eight 

x-ray three 

x-ray five 

oscar six 

alpha two 

lima six 

tango six 

papa three 

bravo one 

x-ray four 

bravo four 

whiskey five 

echo two 

echo five 

quebec one 

echo three 

charlie six 

foxtrot three 

quebec two 

victor four 

hotel two 

yankee two 

charlie five 

kilo four 

yankee four 

alpha six 

delta five 

whiskey one 

papa one 

tango five 

lima five 

charlie two 

echo eight 

oscar five 

echo one 

yankee one 

bravo three 

kilo three 

zulu five 

alpha five 

zulu two 

delta three 

delta six 

victor three 

papa six 

kilo five 

yankee six 

delta two 

charlie three 

alpha three 

charlie eight 

hotel eight 

echo six 

zulu six 

kilo six 

tango eight 

x-ray two 

alpha one 

zulu eight 

lima eight 

oscar eight 

lima one 

delta eight 

whiskey eight 

oscar one 

zulu three 

papa five 

victor eight 

zulu four 

papa eight 

x-ray one 

alpha eight 

victor one 

zulu one 

kilo one 
20 

INTENTIONALLY LEFT BLANK
21 

Appendix B. Results From the DynaSpeak Software for Individual Talkers 

Table B-1. Results from the DynaSpeak software for individual talkers. (Recordings were made from the Gentex 

noise-canceling boom microphone.) 

Error for Words 

subj no. CAT_90_ 

tank 

CAT_110_ 

tank 

commands_ 

90_tank 

commands_

110_tank 

CAT_90_

impulse 

CAT_110_

impulse 

commands_ 

90_impulse 

commands 

_110_impulse 

1B 3.17 12.70 0.59 5.03 6.35 33.33 2.07 10.65 

2B 7.14 39.29 0.30 19.53 17.06 35.71 2.37 7.40 

3 4.37 27.38 1.18 13.02 10.32 28.17 2.96 8.58 

4 5.16 50.00 0.30 19.23 12.30 57.14 1.18 31.95 

5 0.40 14.29 0.30 8.28 9.52 43.65 3.55 34.62 

6 7.94 36.11 2.66 18.05 21.43 52.38 6.80 36.09 

7 1.98 13.10 0.59 2.96 4.76 23.02 1.18 6.80 

8 2.78 21.83 0.30 2.37 7.54 32.14 1.78 5.33 

9 0.40 32.54 0.89 12.13 15.87 28.97 0.89 10.95 

10 3.17 20.63 0.59 4.44 13.89 37.30 2.66 6.51 

11 9.13 43.25 1.78 20.41 17.46 39.68 4.14 15.98 

12 15.08 88.89 15.08 84.91 31.75 73.02 10.06 64.79 

 

avg 5.06 33.33 2.05 17.53 14.02 40.38 3.30 19.97 

sd 4.21 21.39 4.17 22.28 7.49 14.24 2.68 18.26 

Table B-2. Results from the DynaSpeak software for individual talkers. (Recordings were made from the Temco 

HG-17 bone conduction microphone.) 

Error for Words 

subj no. CAT_90_ 

tank 

CAT_110_ 

tank 

commands_ 

90_tank 

commands_ 

110_tank 

CAT_90_

impulse 

CAT_110_

impulse 

commands_ 

90_impulse 

commands_ 

110_impulse 

1B 45.24 78.57 17.75 43.82 51.57 82.82 18.79 66.86

2B 15.87 41.27 6.12 23.16 32.81 64.29 7.10 44.89

3 16.27 73.02 5.62 58.28 23.41 83.73 8.88 88.17

4 24.61 47.22 4.64 29.91 31.50 78.17 7.69 68.93

5 20.63 67.46 12.43 58.28 36.11 88.10 25.44 90.83

6 32.54 67.46 22.49 63.02 62.30 80.95 39.35 77.51

7 25.00 60.32 7.10 26.33 17.86 76.19 5.92 69.23

8 21.83 46.54 2.37 17.75 30.16 66.80 6.96 46.27

9 20.08 61.54 4.58 44.08 30.71 75.79 7.60 62.13

10 25.78 61.11 10.20 47.63 44.14 79.76 27.76 70.43

11 26.89 55.04 12.78 30.77 27.78 69.47 11.47 52.30

12 29.39 86.61 13.02 91.42 46.83 89.29 27.57 91.88

 

avg 25.34 62.18 9.93 44.54 36.27 77.95 16.21 69.12

sd 7.99 13.49 6.01 20.97 12.67 7.93 11.23 16.15
22 

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