USDA Forest Service Research Note RMRS-RN-38. 2009 1

United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

Research Note

RMRS-RN-38

January 2009

Digital Voice Recording: An Efficient 

Alternative for Data Collection

Mark A. Rumble1, Thomas M. Juntti2, 

Thomas W. Bonnot3

, and Joshua J. Millspaugh4

Abstract––Study designs are usually constrained by logistical and budgetary considerations that 

can affect the depth and breadth of the research. Little attention has been paid to increasing the 

efficiency of data recording. Digital voice recording and translation may offer improved efficiency 

of field personnel. Using this technology, we increased our data collection by 55 percent. Digital 

voice recording was useful for the intense, repetitive, and structured data set we collected on 

black-backed woodpecker (Picoides arcticus) nest sites and perhaps for other studies.

Introduction

The time and resources committed to measuring and 

recording data are important considerations in the design 

of ecological studies. Because field sampling is often time 

and labor intensive, researchers designing field studies 

must sometimes make compromises between the desired 

sample size, breadth, and detail of the data to be collected 

and labor costs and efforts associated with the sampling 

approach. These compromises must be made within the 

context of temporal and logistical constraints.

Little effort has been devoted to increasing the ef‐

ficiency of recording data, although data loggers that 

allow for electronic data recording have been available 

to field researchers for more than a decade. Many biolo‐

gists record data on paper and subsequently enter it into a 

computer back at the lab. Recent advances in technology 

have made digital voice recording (DVR) systems and 

Rumble, Mark A.; Juntti, Thomas M.; Bonnot, Thomas W.; 

Millspaugh, Joshua J. 2009. Digital voice recording: an efficient 

alternative for data collection. Res. Note RMRS‐RN‐38. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 3 p. 1 Research Wildlife Biologist, Rocky Mountain Research Station, 

Rapid City, SD. 2 Biological Technician, Rocky Mountain Research Station, 

Rapid City, SD. 3 Research Associate, Department of Wildlife and Fisheries 

Sciences at the University of Missouri, Columbia. 4 Associate Professor, Department of Wildlife and Fisheries 

Sciences at the University of Missouri, Columbia.

voice recognition software for recording field data avail‐

able to biologists. The medical profession has been the 

primary user of these systems (Happe and others 2003). 

Using a DVR system, data is recorded with voice oper‐

ated digital recorders that have hands-free microphone 

headsets. The data is then transcribed into suitable formats 

for analyses using voice recognition software. White and 

others (2002) used voice recognition to collect expanded 

data on cowbird (Molothrus ater) breeding behavior in 

a laboratory setting. We are unaware of other ecological 

studies that used or tested the applicability of digital voice 

recording to collect data, perhaps because of unfamiliarity 

with these systems.

Having the ability to speak into a microphone while 

taking measurements allows field crews to record data 

without laying aside their tools, thus, potentially improv‐

ing the efficiency of their field sampling. In this paper, we 

present a case study evaluation of the utility and efficiency 

of recording data using DVR systems. Specifically, this 

case study reports on the improved efficiency of using 

a DVR system versus hand-held data loggers to record 

data during a 2-year study of nest site selection by blackbacked woodpeckers (Picoides arcticus, BBWO).

Study Area and Methods

Field Sampling

Our study protocol included measuring all trees in 81, 

12.5-m fixed-radius plots radiating from a plot centered 

on a black-backed woodpecker nest, and on random sites 

during 2004 and 2005. Specifically, we were character‐

izing forest vegetation to evaluate resource selection 
2 USDA Forest Service Research Note RMRS-RN-38. 2009

in mountain pine beetle (Dendrochonotus ponderosae, 

MPB) infestations. At each plot, we recorded site identi‐

fication information, species, and condition of all stems 

2.54 to 10 cm at diameter‐breast height (dbh). For trees 

≥10 cm dbh, we recorded species, dbh, and condition. In 

addition, we recorded status of the MPB infestation, age 

of snags killed by MPBs (Farris and others 2002), and 

number of egg niches from wood borers (Cerambycidae or 

Buprestidae) for ponderosa pine trees killed by MPBs.

Hand-Held Data Loggers

In 2004, we recorded data using hand-held data re‐

corders (983 g, 11.4 x 24.8 x 5.1 cm) programmed for 

prompted data entry. Because data collection required 

tools for measurements and took place on steep ter‐

rain with extensive amounts of large woody debris, the 

most efficient use of personnel was usually two-person 

crews—an observer who verified the plot boundary using 

a 10-factor prism (Mannel and others 2006), collected the 

measurements, and called out the responses and a recorder 

who held the target object over the center of the plots 

and keyed values into the field data logger. These data 

were transferred as fixed‐width ASCII text from field 

data loggers to PCs using HyperTerminal1

 (Hilegraeve, 

Inc. 1999, Monroe, MI; bundled with Windows®). We 

recorded data entry errors (we were aware of) on small 

field tablets and corrected them in a text editor.

Digital Voice Recording Systems

In 2005, we recorded data using a DVR system 

comprised of a voice operated digital recorder (Sony 

ICD‐MS‐5151

, Sony Corporation of America, Park Ridge, 

NJ) and a noise canceling, hands-free headset micro‐

phone equipped with a mute switch microphone cable 

(Andrea ANC‐7001

, Andrea Electronics, Melville, NY). 

The digital recorder stopped recording after 4 seconds 

without an input signal. The DVR connected to a PC 

by a USB port and could be used like a flash memory 

device. We transferred digital voice data to a personal 

computer nightly and translated it to ASCII text using 

the voice recognition software Dragon Naturally 

Speaking (DNS) 7.31

 (Nuance Communications, Inc. 

2003, 1 Wayside Road, Burlington, MA). Digital voice 

files were saved as *.MSV files as data backup. Re‐

cording errors spoken into the recorder were obvious 

after translation to ASCII text. Errors were replayed for 

audio verification and then corrected. DNS develops 

a probabilistic voice model for individuals using both 

training data and corrected translation errors. Thus, 

repeated corrections of translation errors improved 

translation in subsequent sessions. ASCII text files 

were then edited to a suitable format for data analyses 

using text editor software.

Results and Discussion

In 2004, most field crews were comprised of two 

people. Occasionally, we had a one‐person crew and 

on 2 days, we had a three-person field crew. Although 

exact crew size data are lacking, we conservatively es‐

timated our field crews averaged 1.75 people. In 2004, 

we measured 36 sites (2,916 plots or 69,173 trees) and 

averaged 14.2 ± 0.6 (ˉx ± SE) plots/person/day of field 

work. In 2005, we measured 60 sites (4,860 plots or 

122,227 trees) and averaged 22.0 ± 0.8 plots/person/day 

of field work. Using a DVR system increased our effi‐

ciency by 55 percent in terms of plots measured/person/

day. Additionally, in a study on the social interaction of 

cowbird, White and others (2002) improved the amount 

of data and breadth of the study. Generally, data from 8 

hrs in the field resulted in 1 to 11⁄2 hrs of recording time, 

and the translation and editing of voice data could be ac‐

complished in <1 hr. We strongly recommend that voice 

translation and editing be completed each day.

While DNS has some reserved words for specific com‐

mands, it did not always recognize them when we said 

them. However, our data were highly structured, with 

each tree resulting in a separate data line. Consequently, 

translation errors of reserved words and other errors were 

easy to detect in the text files and could be corrected us‐

ing the text editor. Some common translation errors were 

corrected using macros programmed into the text editing 

software or with the search and replace function.

Use of DVRs required that one person on the field crew 

be knowledgeable of the voice translation software. This 

required 1 to 2 days of preparation. Other members of 

the field crew could develop voice models in about 4 hrs. 

We also discovered that voice models could be copied 

and used by other technicians of the same sex and with 

similar accents after several iterations of interpretation 

and correction of a sample data set. Programming the 

field data loggers and training the crews required com‐

parable effort.

Careful enunciation of words in the field improved 

the accuracy of the data translation. Although the mi‐

crophones were noise canceling, more translation and 

post‐processing was necessary for data recorded on 

1 Use of brand name does not constitute endorsement by the U.S. 

Forest Service to the exclusion of other products that are acceptable.
USDA Forest Service Research Note RMRS-RN-38. 2009 3

windy days. DNS translated English words better than 

scientific names, abbreviations, or codes. For some data, 

it was easier to change our input than to train the voice 

model. For example, “forb” was usually translated as 

four, so we substituted “herb,” and with some training of 

the software, it translated well. Some numbers, such as 

21, were translated as words (for example, twenty‐one), 

while others were translated as word homophones (for 

example, 4 as four or for; and 2 as two or to). We found 

it difficult to train DNS to recognize these numbers. 

Unfortunately, attempts to train the software to recognize 

homophones do not substantially improve the translation 

error rates (Happe and others 2003). However, our data 

consisted of a limited range of responses, so translation 

errors were easily identified and corrected.

Management Implications

There are many potential applications to using DVRs 

to record data that could significantly change the future 

of data entry. The following are a few examples of the 

benefits we received by using DVR to record our re‐

search data.

• Recording research data using digital voice recording 

allowed for more efficient allocation of personnel 

resources than did recording data using hand-held 

data loggers.

• Technicians were unencumbered while taking 

measurements and data were recorded by simply 

speaking the values measured.

• Once translated into ASCII text files, data could 

easily be cleaned up and ready for analyses.

• In addition to this study, we used digital voice 

recording when measuring prairie vegetation, 

including herbaceous cover and shrub density 

measurements, and when characterizing drumming 

logs used by ruffed grouse (Bonasa ubmellus).

• Equipment and software for digital voice recording 

is inexpensive, and the initial cost of the system 

can be recovered by the time that is saved record‐

ing data.

References

Farris, K.L.; Garton, E.O.; Heglund, J.P.; Zack, S.; Shea, P.J. 

2002. Woodpecker foraging and the successional decay of pon‐

derosa pine. Pp. 237-246. In: Laudenslayer Jr., W.F.; Shea, P.J.; 

Valentine, B.E.; Weatherspoon, C.P.; Lisle, T.E. Proceedings of 

the symposium on the ecology and management of dead wood 

in Western forests. Gen. Tech. Rep. PSW-GTR-81. Albany, 

CA: U.S. Department of Agriculture, Forest Service, Pacific 

Southwest Research Station. 949 p.

Happe, A.; Pouliquen, B.; Burgun, A.; Cuccia, M.; Le Beux, P. 

2003. Automatic concept extraction from spoken medical reports. 

International Journal of Medical Informatics 70:255-263.

Mannel, S.; Rumble, M.A.; Price, M.; Juntti, T.M.; Hua, D. 2006. 

Comparison of combinations of sighting devices and target 

objects for establishing circular plots in the field. Res. Pap. 

RMRS‐RP‐60. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 5 p.

White, D.J.; King, A.P.; Duncan, S.D. 2002. Voice recognition 

technology as a tool for behavioral research. Behavior Research 

Methods, Instruments, and Computers. 34:1-5.

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