AAC Performance: The Elements of Communication Rate
Katya Hill, Ph.D. CCC-slp (Edinboro University
of Pennsylvania)
Barry Romich, P.E. (University of Pittsburgh)
Rebecca Holko, B.A., (Edinboro University of Pennsylvania)
ASHA 2001
New Orleans
Abstract
AAC communication rate using assistive technology is a function of various
factors, including language representation method use, selection rate, and
errors. Empirical data from the language samples of five individuals who were
subjects in a controlled study provide clinically useful insights into optimizing
communication rate.
Background
Measuring AAC communication rate can provide valuable information on the
efficacy of various rate-enhancement strategies to improve interactive communication.
However, we make decisions about the benefits of available strategies without
much assistance from a research base (Beukelman and Miranda, 1998). The content
of this paper focuses on communication using assistive technology.
People who rely on AAC value communication rate highly as a trait they desire in an AAC system. Their communication rate is almost always far slower than natural speech. Therefore, every effort must be made to maximize communication rate for this population. Communication rate is influenced by many factors. Elements of rate include 1) the language representation method (LRM) employed for accessing core vocabulary, 2) the selection rate, and 3) errors.
Recent work has resulted in AAC language activity monitoring (LAM) for clinical use, funded in part by the National Institute for Deafness and Other Communication Disorders of NIH (Romich and Hill, 1999; Hill and Romich, 1999). These developments have made available tools to collect language sample quantitative data on which to base assessment and intervention decisions. LAM data is being used to produce quantitative AAC performance summary measures. A set of fourteen summary measures (Hill and Romich, 2001) includes 1) average communication rate, 2) peak communication rate, 3) communication rate by language representation method, 4) language representation method usage for spontaneous communication, 5) selection rate, 6) word selection errors per word selected, and 7) spelling errors per word spelled.
Objectives
The objectives of this work were to 1) demonstrate the measurement of
communication rate based on normal LAM data, 2) begin the establishment of
indices of performance in the areas of communication rate for individuals
who rely on AAC and use direct selection, and 3) foster an understanding of
the factors that affect communication rate.
Method
Language samples from LAM are reported in the following format:
20:37:00 "content of the language event".
The time stamp is a 24 hour format with one second resolution.
LAM data were collected from five individuals who were subjects in a controlled study. Data were analyzed using various computer programs and manual methods. For each individual, both average and peak communication rate was calculated along with the five other identified measures that contribute to communication rate.
Average and peak communication rate are based on the time required to construct utterances and the number of words included in the utterance (Romich and Hill, 2000). The peak rate is the highest rate for an utterance longer than the mean length of utterance (MLU) for the sample. The procedure for making this calculation has been included in ACQUA (Augmentative Communication Quantitative Analysis) (Lesher, et.al, 2000) which was used for this analysis. These measures are reported in words per minute.
Communication rate by language representation method first identifies the method used to generate each word. Methods identified are 1) single meaning pictures, 2) spelling, 3) word prediction, 4) orthographic word selection, and 5) semantic compaction. Then the rates for generating each word in the method category are averaged. By knowing how the communication rates for an individual compare, the AAC clinician can enhance communication rate by moving the most commonly used words into the method with the fastest rate. These measures are reported in words per minute.
Language representation method usage is simply the ratio of the number of words generated using each method to the total number of words. These measures are reported as a percentage.
Selection rate is a measure of the information input rate from the individual to the AAC device (Romich, Hill & Spaeth, 2001). This definition takes into account the number of locations in the selection array and the speed with which they can be selected. This measure is reported in bits per second.
Errors adversely impact communication rate. Word selection errors per word selected and spelling errors per word spelled are both reported as a percentage.
Results of these seven summary measures for all five subjects will be included in the presentation.
Discussion
The availability of methods for measuring communication rate has implications
in the areas of clinical intervention, outcomes measurement, and research.
With minimal training, AAC clinicians can calculate these measures with commonly
available software. Knowledge of these factors influencing communication rate
can guide therapy. The end result of the use of these methods is the enhanced
communication and higher personal achievement of people who rely on AAC.
References
Beukelman DR & Miranda P (1998). Augmentative and Alternative Communication:
Management of Severe Communication Disorders in Children and Adults.
2nd Edition. Baltimore: Paul H. Brookes Publishing Co.
Hill, K & Romich, B (1999). AAC language activity monitoring and analysis for clinical intervention and research outcomes. CSUN. Los Angeles, CA.
Hill, K. & Romich, B (2001) A summary measure clinical report for characterizing AAC performance. Proceedings of the RESNA 2000 Annual Conference. Reno, NV.
Lesher, G.W., Rinkus, G.J., Moulton, B.J., & Higginbotham, D.J. (2000). Logging and Analysis of Augmentative Communication. Proceedings of the RESNA 2000 Annual Conference, 82-85.
Romich BA & Hill KJ (1999). A language activity monitor for AAC and writing systems: Clinical intervention, outcomes measurement, and research. Proceedings of the RESNA ’99 Annual Conference. Long Beach, CA. pp 19-21.
Romich, B and Hill, K, (2000). AAC communication rate measurement: tools for clinical use. Proceedings of the RESNA 2000 Annual Conference. Orlando, FL. pp 58-60.
Romich, B, Hill, K & Spaeth, D (2001). AAC selection rate measurement: a method for clinical use based on spelling. Proceedings of the RESNA 2001 Annual Conference. Reno, NV.
