Matthew V. Ramsey, M.Ed.
The Use of Voice
Teaching the Writing Process
On my eighteenth birthday, my older sister, Molly, gave me a keychain that read: “Ask me about my lobotomy”; she wasn’t kidding.
At the age of fifteen, I was a moderately motivated high school sophomore, with a 3.7 grade point average; then, one day, in the hallway between classes, I sustained a massive stroke; my fouteenth and, thankfully, my last.
From there, things changed dramatically. After spending one month in the hospital, comatose, I underwent surgery to extract the mass of blood vessels that had existed in the left hemisphere of my brain, since before I was born.
This was a surgery that saved my life yet, left me with a profound short-term memory deficit. Suddenly, the simplest tasks I would need to accomplish, as a reader, as a writer and as a thinker, became amazingly difficult.
Over the course of the next 15 years, I used every means possible to overcome my disability enduring the rigors of occupational and cognitive therapy and consulting with numerous specialists, each of whom made suggestions as to how I could compensate for my memory deficit.
Once in undergraduate school, I was able to register for classes as an officially learning-disabled student and I enrolled in a specific type of class, specifically geared to provide me compensatory strategies. The strategies were intended to allow me to determine learning methods that would circumvent the effects of my disability and allow me to succeed in the courses I was “really there to take”.
But, the application of the strategies was never really connected to authentic life situations and they were presented as merely theoretical, were never applied to a real learning situation, and, while they provided me with some workarounds for compen-sating for my learning disability, they did not offer a comprehensively effective approach.
Eventually, after having transferred from school to school, I was able to graduate with a Bachelor of Arts degree, in Writing, with which I was never really able to maintain more than contractual employment.
Only three years ago, while having my disability re-assessed, prior to re-entering academia, an evaluator noticed that my verbal skills were in the 99th percentile of achievement; while my other cognitive faculties, such as my ability to read for comprehension and my short-term recall were disproportionately lower.
Almost as an afterthought, he asserted I might want to try using voice recognition software. Little did he know that his comment would have so profound an effect upon my cognition.
The use of voice recognition software has been able to fill in communicative gaps, both in transmitting and in receiving information.
As well, it has been able to remedy a specific part of my disability that affected my reading, writing, speaking, listening, hearing and thinking.
In addition, my consistent usage of voice recognition software has allowed me to reestablish damaged neuron passages, improving my memory.
Based on my experiences in learning and in my use of the software, I am convinced that it has the potential to help other students learn to write, learn to speak and learn to communicate, more effectively.
The following is an overview of what voice recognition software is, how the technology has been used in educational settings, how it has been used as an assisted technology for accommodating learning disabled students that allows these students to circumvent cognitive and procedural obstacles, how it can be, potentially, used to teach the writing process to all students and, finally, what suggestions can be made for potential future research.
What is Voice
Essentially, voice recognition software captures what is spoken into a microphone and converts the speech into text that appears on a text document; this product has been existent for almost 20 years, having first been used by workplace professionals to record pertinent job-related notes and to eliminate the need for the transcription services of an office assistant.
However, it was not long before educators began to hypothesize how the tool could be used in educational settings. Early versions of voice recognition software operated using a model of discrete speech, as the user spoke the computer translated the speech word by word into text. This methodology required long sessions of training in order for the computer to recognize each writer’s individual speech pattern and pronunciation.
Today’s voice recognition software systems use a new technology, called continuous speech. Continuous speech allows users to talk at a more conversational speed and the computer is able to use context clues to record the wording. Today’s VRS is faster, more accurate and easier to train than previous versions of the software.
How Voice Recognition Software
Has Been Used
Primarily, voice recognition software has been used as an assistive technology for facilitating learning disabled students’ compensation for specific cognitive disabilities; there has not been extensive research conducted to determine the potential benefits of voice recognition software in other educational settings or for non-disabled student.
The research that has been conduc-ted shows promising benefits for learning disabled students who have access to the technology.
Overview of Research
Voice Recognition Software
Studies have been taken place that have centered on the use of voice recognition software by students of every education age group, from elementary through undergraduate school. Improvements were determined to be made by students who were engaged in a variety of writing tasks, from improvement in speed and ease of writing, to improvements in spelling and quality of writing.
High School Students
In 1991, Dr. Marshall Raskind and Eleanor L. Higgins developed a long-term research study focusing on dyslexic students and the use of voice recognition software. Two studies of dyslexic students, ages 9 through 18, found improvements evident in reading and spelling proficiency when students use the software to write.
Using voice recognition software, as students would say a word and see the word, correctly spelled, flash up onto the computer display.
The researchers realized that this ability for students to monitor their writing proved helpful, allowing students the ability to memorize correct spellings of words. (Sanderson, 1999)
In 1994, Graham worked with learning disabled students who were approximately 9 to 11 years old and who demonstrated increased speed in completing writing tasks, through the use of voice recognition software. (De La Paz, 1999)
In 1998, a study of 12 dyslexic students, aged 14 to 16, demon-strated that students who were trained in using the software for just 10 hours were able to dictate and achieve accuracy rates of 82-90%; and, despite initial frustrations, the usage of voice recognition software improved the quality of the writing for each of the 12 students. (Miles, Martin, Owen, 1998)
Higgins and Raskind, in 2000, studied 39 students, varying in age from 9 to 18, and determined that benefits were retained by the students in the areas of reading comprehension, spelling and word recognition, when they used voice recognition software.
Researchers also determined that students’ monitoring skills improved as the students dictated writing-based work. This success, in turn, lead students to experience improvements in spelling proficiency and ability to achieve higher scores on tests of working memory.
On the undergraduate level, the usage of voice recognition tech-nology has also been found to be effective in facilitating students’ abilities to write.
During an early study by Dr. Marshall Raskind at the California State University at Northridge, 26 undergraduate dyslexic students who used discrete voice recognition technology created writing samples that could not differentiated from samples produced by their non-learning disabled peers.
The learning disabled students were able to demonstrate a broader usage of vocabulary than was possible for them to produce, prior to the study and better writing skills. (Austen) Another study at the University of Nottingham involving six dyslexic students corroborated the North-ridge study’s results and showed that using voice recognition software provided increased student’s ability to compete and produce work at the same qualitative level as did their non-learning disabled peers. (Sanderson, 1999)
In 1995, at California State University, Northridge, researchers compared learning disabled students’ performances on a written proficiency examination to their non-learning disabled peers’ rates of achievement on the same test; learning disabled students who used voice recognition software in taking the test achieved the same distribution of scores that the non-learning disabled students achieved.
Without the access to voice recognition software, the same learning disabled group of students achieved significantly lower scores. (Higgins & Zvi, 1995)
In another research study of learning disabled post-secondary students, conducted by Higgins and Raskind, students’ writing samples produced with voice recognition software were compared to samples by the same students, produced using a transcriber or produced with no assistance.
The samples that were written with voice recognition software were assigned higher holistic writing scores than those that were created with a transcriber or without any assistance. (Higgins & Zvi, 1995)
During a direct research study of voice recognition software, conducted by Roberts and Stodden in 2005, learning disabled post-secondary students were provided a training program that taught them to use Dragon NaturallySpeaking.
The researchers, then, followed the students’ progress over the course of the semester to determine the overall effect voice recognition software had on students’ writing process.
The study’s intent was to determine students’ willingness to continue to use voice recognition software throughout the semester and to determine if students would utilize the technology to complete both educational and personal writing tasks.
Researchers found that many students stopped using the software after initial training because the time requirements for achieving profic-iency in using the software were too great.
Students expressed interest in trying the software again when they did not have to learn it simultaneously while keeping up with the work required for their other classes; however, for the students that stuck with the program, they used the software successfully in both educational and personal writing projects.
Researchers also looked at the quality of the writing produced with voice recognition software as compared to earlier writing samples. According to Fry’s readability graph, writing samples, submitted by the students, were improved through the use of voice recognition software.
The scores that were achieved before the usage of voice recognition software averaged 4.5. Following students’ introduction and usage of the software, scores that were achieved had an average of 6.5.
For some students taking part in the study, voice recognition software allowed them to circumvent learning deficits. Students, who successfully maintained the diligence and desire to continue using voice recognition software, eventually, were able to utilize the product effectively, allowing the software to compensate for the student’s specific, communicative deficits.
One student, for example, used the software to improve her spelling skills, such to enhance her existing verbal strengths.
Another student used the software to get her thoughts down on paper before she lost her train of thought, thereby, compensating for her weak ability to organize and process information. (Roberts & Stodden, 2005)
There are some commonalities that can be derived from the research studies above. Most show significant benefits for learning disabled students; however, these studies also detail common obstacles to keep in mind when attempting to use voice recognition software with learning disabled students.
The researchers also offer insight into how a teacher can assess learning disabled students to determine whether voice recognition software may be a good fit for them. An overview of common findings is offered below.
Benefits of Voice Recognition
Software for Learning Disabled
Ninety percent of learning disabled adults report having encountered problems with writing or spelling; the most commonly experienced problems, being documented as occurring in the specific activities of handwriting, spelling and written expression. (Vogel & Moran, 1982) Voice recognition software promotes learning disabled students’ acquisition of better spelling and writing skills.
This is especially true if the student’s verbal abilities eclipse his/her ability to generate text through written means. (National Center to Improve Practice, 2003)
Overall, when learning disabled students are able to improve the legibility of their work and decrease the number of spelling and grammar errors in their work, their grades improve; (Graham, 1999; Forgrave, 2002) and, it has been noted that, when students realize that voice recognition software allows them to produce neat work, their motivation to write increases. (Forgrave, 2002)
Even more significantly, voice recog-nition software allows learning disabled students to get their thoughts down on paper quickly, allowing their thoughts to be communicated and recorded before the ideas can be forgotten, obscured or transposed by the student’s damaged or ineffective cognition. (Roberts & Stodden, 2005; Forgrave, 2002)
The technology allows dyslexic students to produce an uninter-rupted flow of ideas, making the complete recitation of these much more successful and meaningful, because the student does not have to concentrate on spelling, writing or typing the words which helps dyslexic students compensate for weakness in their ability to multi-task.
In the absence of voice recognition software, these students have to coordinate and perform the processes simultaneously. (Litten, 1999)
Voice Recognition Software’s use has been proven to help students accomplish more writing and, as well, more writing independently. (Roberts & Stodden, 2005) They produce longer, more complex bodies of text.
A meta-analysis of five studies that were conducted on voice recognition software’s usage found that stories students wrote, using voice recognition software, were longer, were more grammatically correct and were more complex than essays written through other means. (Forgrave, 2002)
Many studies of VRS targeted dys-lexic students and reviewed the impact of the different versions of the software on students’ writing abilities. The earlier version, which utilized discrete speech voice recognition technology, proved to be more effective.
It required a slower, more deliberate, word-by-word, recitation of the students’ word choices which allowed dyslexic students to evaluate and confirm the accuracy of the word recognition and spelling with greater ease. (Austen)
Voice recognition software enables dyslexic students to exploit their full language capabilities by providing effective way to monitor their writing. When using the software, dyslexic learners demonstrate improvement in recognizing and spelling the words that exist in their own oral vocabularies. (Austen)
This helps dyslexic learners become more efficient, as writers, because alleviates the need to form words that is necessary in the physical act of writing or typing them. (Litten, 1999)
Obstacles Learning Disabled
Students Face in Using Voice
Although the potential benefits that are presented by the availability of voice recognition software to LD students are great, there are inherent obstacles that students have encountered in the product.
These problems can be sorted into one of two categories: time-based constraints and the inherent difficulty experienced in using the software.
Students may find the process of mastering the software’s usage to be quite intimidating; this is especially true if students are attempting to complete the training process while also fulfilling other academic obligations. (Sanderson, 1999; Roberts and Stodden, 2005)
If students had already developed compensatory strategies that they had found to be successful, students were less willing to go through the time-consuming process to become proficient in the usage of voice recognition software. (Roberts and Stodden, 2005)
The training process that is necessary for voice recognition software usage can be difficult and time-consuming. Students must remember special commands to operate the software, successfully. Some of these commands are quite difficult to remember, especially for memory deficit students. (Forgrave, 2002) Coughing and laughing can thwart adequate dictation from being accomplished. (De La Paz, 1999)
If the student does not correct the errors made by the program, the accuracy of the user profile is diminished and the user profile can be corrupted. (McEwan, 1998)(Forgrave, 2002)
Students need extensive instruction and monitoring in the comprehen-sion and usage of the software for voice recognition software to be a successful tool for them. (Higgins and Raskind, 2000; Forgrave, 2002)
For example, undergraduate dyslexic students who took part in a research study at the University of Notting-ham reported experiencing difficul-ties in the training process of voice recognition software.
During the process to train the software to his or her voice, the program required students to read the text completely and accurately, using one attempt. Substitution of words resulted in the production of a less accurate voice profile and negatively impacted the funct-ionality of the program.
Accuracy was also compromised when students would mispronounce new vocabulary words; students lost confidence in the technology and their ability to learn it successfully. (Sanderson, 1999)
Dyslexic students, in particular, faced additional challenges. They had difficulty remembering voice commands, inherent to successful usage of the software.
For dyslexics writers, who think and communicate more readily through visual means and who use symbols and pictures in place of words, having to translate thoughts that are visual into correct grammar and usage form can be overwhelming.
Making amendments to documents was determined to be more frust-rating for the dyslexic students because these students sometimes experience a limited working memory; often, they did not remember the specific words they used, originally, to create a body of text. (Sanderson, 1999)
Dyslexic students also have difficulty automating processes and multi-tasking. (Nicholson and Fawcett, 1990) Learning voice recognition software requires many different, but related processes to occur, simultaneously.
These processes include:
*identifying the concepts of an argument,
*deciding how to express the argument,
*choosing task-appropriate vocabulary,
*using correct grammar,
*deciding the hierarchy of the idea that needs to be communicated,
*completing the task of spontaneously speaking in grammatical sentences,
*verbalizing punctuation marks,
*monitoring words and sentences for accuracy as they appear on the screen, and
*holding the memory of a correction that is necessary to be made for the sake of going back and revising it.
Therefore, providing sufficient training time and support for dyslexic students is imperative. (Sanderson, 1999)
Characteristics of Students
that Benefit from the Use of Voice
Even with the challenges learning the software poses for students, voice recognition technology has the ability to help students bridge the gap and overcome many different disabling factors. Research suggests that students have certain capabilities in order to successfully benefit from the use of voice recognition software; however, none of these is drastically onerous.
Students need to be able to use a computer and speak Standard English. Poor or limited traditional writing skills do not necessarily thwart the successful use of voice recognition software.
In fact, students who have a high need for remediation, including poor written language abilities, poor processing, difficulty in organizing thoughts and putting thoughts into writing, can be helped immensely.
Potential users and benefactors of the software include students and adults with physical disabilities that prevent these individuals from using a keyboard and mouse, head injury sufferers and aphasia victims, dyslexic students and students with other types of learning disabilities. (National Center to Improve Practice, 2003)
Good prospects include students who have limited compensation strategies for written language problems and those with have motivational issues with writing tasks. (Roberts and Stodden, 2005) Cognitively, students need to be able to multitask; they need to be able to compose and monitor text while operating the system through oral commands.
Linguistically, students must be able to reckon the contrast between spoken and written English. From a behavioral standpoint, students must have the motivation to learn the software and improve their own writing skills, persevering through the training process and accepting the reality that they are using a different system than many of their peers. (National Center to Improve Practice, 2003)
Included in the research, were narrative representations of successful users of voice recognition software. Jason was 14 years old when he suffered a brain injury in a boating accident. He suffered from aphasia, apraxia and dyslexia; he had suffered from these afflictions before his accident and began to use voice recognition software with a tutor in the classroom of his high school.
Jason was able to graduate from high school and go on to college. (National Center to Improve Practice, 2003)
Sara was a fifth grader who had terrible handwriting, idiosyncratic spelling and difficulty in learning how to use a keyboard. After learning to use voice recognition software, she wrote a 10-page story and, eventually, won a creative writing award. It made the difference in her struggle to learn to write proficiently. (National Center to Improve Practice, 2003)
Using Voice Recognition Software
Teaching the Writing Process
Up to this point, voice recognition software has been used for adaptive technology purposes; however, its potential for broader use by writing students, in the future, remains promising. Today’s computers are faster and newer versions of the software are becoming more and more accurate.
One day, voice recognition software will change how people work, how people interact with technology and, most importantly, how people write. Because there is little research published on mainstream students’ use of voice recognition software, Lee Honeycutt, in the article “Researching the Use of Voice Recognition Software”, reviewed studies conducted on the use of traditional dictation in the writing process and considered theories of writing development and their implication for the use of voice recognition software.
Below is a brief synopsis of Honey-cutt’s review followed by a look at how voice recognition software can be used to teach the writing process.
Studies Pertaining to Dictation
Early studies of dictation in educat-ional settings compared written pro-ducts derived from dictation to writ-ing samples that were achieved through silent writing.
The results varied, depending on student age, student approach to writing and the type of writing being produced by the student; however, there are too few studies and too many variables in play to make meaningful conclusions.
In an early study of dictated writing contrasted to silent writing using novice and experienced dictators, John D. Gould (1978) determined no qualitative difference between dictated letters and handwritten letters, except that he noted that the dictated letters were longer and composed more quickly.
Deborah McCutcheon, in 1987, also contrasted students’ silent writing to writing produced through dictation. McCutcheon‘s students, ages 9, 11 and 13 composed narratives and essays which were analyzed for text length, for structural elaboration, and for coherence.
She found that, as a rule, narratives were longer and more coherent than were the essays. Dictated essays were the least coherent; dictation seemed to encourage simple, sequential writing. McCutcheon theorized that silent writing was more productive for essay writing because the process was slower, mechanically and gave students more time to think and process information. (McCutcheon, 1987)
Reece and Cumming (1996) asked 16 undergraduate psychology students to write four untimed compositions. Some students, producing writing samples, were provided time for planning, and others received no time for planning. Each group was asked to write compositions using conventional means and using dictation. Reece and Cumming found that essays that were planned and dictated received the highest scores.
In another study, Cumming and Reese (1996) worked with 30 fifth and sixth grade students. Because voice recognition software was not fully developed at that time, researchers simulated a similar experience for the students by using a hidden transcriber who typed the text students dictated, in real time, so that it would appear instantaneously on the screen in front of the students.
This system was referred to as a Listening Word Processor (LWP). Essays that were produced with the LWP received holistic writing scores that were higher than scores, achievable through either silent writing word or traditional dictation.
A second study, of fifth and sixth graders with particularly poor writing skills, demonstrated that LWP-produced essays again achieved higher holistic scores; however, these scores were only slightly better than scores that were recorded for students who used traditional dictation methods.
Theories of Writing Development
Their Implications for Writing
Robert Zoellner (1969) proposed a new approach to writing education that facilitated students’ use of their oral proficiencies to fine tune and perfect their written communication.
Zoellner championed the concept of intermodal transfer, wherein students develop scribal modality from their pre-existing vocal modality.
According to Zoellner, intermodal integration is achieved when students’ writing improves, presenting a sense of voice. Simultaneously, their speech develops an increasingly-literate quality. Using voice recognition software would extend Zoellner’s theory by integrating speech and writing, even more.
In 1981, Barry Kroll stipulated four phases of writing development in which the writer differentiates between speaking and composing. In the preparation stage, students learn the technical skills which allow them to write the words that are already a part of the student’s vocabulary. In the consolidation stage, students’ writing and speech become integrated. In this stage, writing is “talk written down”.
Next, is the differentiation stage, in which students learn to contrast the differences in structure and style that exists between speaking and writing. Writing, they learn is formal and explicit.
Whereas, speaking is casual and context-dependent. Mature writers who are aware of the difference that exists between written and spoken language can make choices about how to use language to determine appropriate voice and stylistic aspects of the intended communication, according to the writing’s context and audience.
According to Kroll’s model, using voice recognition software in the differentiation stage and may retard students’ ability to move from the consolidation to the differentiation stage. It may be that, when students are acquiring the ability to plan texts, to review and to revise, using voice recognition software may hinder development.
However, using the same model, voice recognition software could prove to be effective as a tool in the early stages of writing processes, as it can help students practice the act of composition and arrive at a more authentic notion of narrative language, before they are actually beginning to write.
Possible Uses of VRS
The following possible uses of voice recognition software in teaching the writing process are drawn from the descriptions of activities that are contained in the research studies, already discussed; they come, as well, from my personal experiences.
There has not been enough research conducted, presently, to make this a complete or definitive listing. These are provided as a starting point from which to conduct possible future research.
Generating and Brainstorming:
Voice Recognition Software allows students to engage in free writing, as a pre-writing exercise that is quick and painless, facilitating students’ ability to move from one thought to another, seamlessly.
The students are able to speak what they already know and are able to concentrate on one concept without having to engage in the additional task of physically writing.
In fact, Charles Lowe has coined the term, “free-speaking”, meaning an enhanced form of free-writing that takes full advantage of the fact that speaking is faster than writing.
Free-speaking, with voice recognition software, eliminates undecipherable notes, offers students the ability to generate ideas, without interruption, and links the act of content generation to the natural rhythms and cadences of human speech.
Thereby, students’ creativity is not stifled by grammar and usage concerns when they are producing viable text.
Voice recognition software, also, is a valuable tool for use in note-taking. Students can take notes with VRS, as they read, allowing them to paraphrase what has been read, giving them greater exposure to the information that they are attempting to learn. The act of paraphrasing the information helps students imprint the information into their crystal memories.
Through the deep processing of the new information, students are enabled to connect newly-acquired information to information they already know. In effect, the use of voice recognition software closes the gap between new and old informa-tion and works to speed up students’ cognitive processing.
When students work with a tutor or a peer in brainstorming an idea for a piece of writing, they can mute the voice recognition technology system’s microphone and maintain that muted status, during the discussion; then, when it is prudent, they can release the mute button and seamlessly capture the spoken content of the brainstorming activity before resuming the discussion. In reality, this is one example of voice recognition technology’s amazing potential for use in collaborative environments.
Effective organization of text in the pre-writing stage has always been an important part of the writing process; it is even more effective when it is accomplished with the use of voice recognition software; in an applied learning and communication environment, voice recognition software, as it is used with Microsoft Word’s outlining function, allows a writer the ability to produce a usable outline, quickly and easily.
This outline provides a template for the structure of the student’s essay. And, using the outline’s headers and sub-headers to determine where the writer needs to insert rhetorical cues, the outline allows the writer maintain awareness of the essay’s communicative direction; this stimulates sentence creation. Once the writer realizes that the outline will help them maintain coherence, they will be free to devote his/her attention to other components of the writing task.
In one study of 16 undergraduate student writers, writing samples produced with pre-planning and without pre-planning were compared. Samples that were produced with pre-planning were of a higher quality than essays produced without the planning process, and even more so if those samples were produced using dictation. (Reese and Cumming, 1996)
Producing the Rough Draft:
The use of voice recognition software can provide a student with an effective means to produce a rough draft. After using the software to outline and pre-plan their writing, students can assign rhetorical cues to indicate hierarchy of information or level of specificity to each section of their outline to help spur sentence and paragraph creation and guide their reader’s comprehension.
Writers can refer to and use the outline as a syntactic guide by which to monitor the uniformity, balance and communicative effect of the composition. As a post-writing task, students can be taught how to identify and trace the rhetorical pattern in their rough draft, using rhetorical cues that they have used to trace the flow of information and to allow them to determine if they have presented a balanced and complete treatment of their topic.
Even though it is possible to conduct revisions through verbal commands using voice recognition software, in general VRS is not an effective tool for engaging in the revision process; it is easier to revise, using a standard keyboard.
Ben Schneiderman (2000) suggests that voice recognition software makes the act of revision more difficult, because the same part of the brain is used for both problem solving and speech.
However, in a case study, conducted in two remedial learning centers, located in Philadelphia, Pennsyl-vania, adult learners used speech synthesis software to evaluate and revise papers they were writing. Text or dictation read-back is a feature inherent in voice recognition software applications. The program reads back the text, just as it appears in the document.
The text read-back feature allows students to self-monitor the sound of their own writing. Students showed improvement in their editing skills including the mastery of punctua-tion, especially commas, as well as the identification of run-on senten-ces and incorrect spellings.
Users were able to listen to what the software read back and contrast it to what they were meaning to convey. (Drexel University, Office of Computing Services, 1995)
Acquiring New Vocabulary:
Vocabulary acquisition is enhanced when students utilize the new vocabulary words in both speaking and in writing. Students can use voice recognition software during reading to paraphrase information as they read and become better acquainted with new vocabulary words.
Eventually, this will help students’ acquire new words and extend their communicative arsenal, both written and spoken. Voice recognition software, used in conjunction with Microsoft Word’s usage correction function, will highlight a new vocabulary word that has been implemented incorrectly; thereby, giving students the ability to monitor their success in using new vocabulary.
Suggestions for Future
Voice recognition software, as a new technology whose functionality improves every year, it is still relatively new. There is still a great deal of research that needs to be conducted on ways it can be used and how its usage will impact communication for all types of writers.
For instance, what are the most effective methods for using voice recognition software in the writing process? Personally, I prefer to use an outline that guides my writing. This seems to be corroborated by early studies; however, little comparative research has been conducted on the topic of best practices in the use of voice recognition technology.
Is there an existent connection between improved writing and improved speech, for students who consistently use voice recognition software?
Can and Should
Writing and Speech
Be More Integrated?
Would a broader approach to rhetoric and integrated approach to oral and written communication improve the critical thinking skills of students?
Lee Honeycutt (2003) questioned whether voice recognition software is more or less effective, when used to compose different genres of writing. Honeycutt also suggested that future research should attempt to determine how the usage of VRS impacts students at different developmental stages in the process of learning to write.
Most studies of voice recognition software have focused on students who have special needs; future research studies need to determine voice recognition software’s effects on mainstream writing students. Could voice recognition software improve the quality of writing that is produced by mainstream students as well?
The effects of voice recognition software on English as a second language students needs to be considered; for example, would the technology’s use help English as a second language students become better enabled to pronounce L2 words?
Would the Software’s
Could voice recognition software be used effectively in the education of English as second language students who are also learning disabled?
When determining how voice recognition software can be most effectively used in education, there are certainly many unanswered questions.
These are questions I hope to play a part in answering; because of the impact voice recognition software has had upon my own life and my own education.
I am convinced of this technology’s potential for me, for other learning disabled students and for all writers.
I need a new keychain that reads: “me about my lobotomy…if you want an earful on voice recognition software’s part it played in steering my life back upon a meaningful path.”
Austen, I. (n.d.). Voice recognition software helping dyslexics. Retrieved March 7, 2008, from Dyslexia Teacher: http://www.dyslexia-teacher.com/t111.html
Boyd, B. F. (2008). Assistive technology for every child. Montissori Life , 20 (1), 30-35.
Bruce, C., Edmundson, A., & Coleman, M. (2003). Writing with voice: an investigation of the use of a voice recognition system for a man with aphasia. International Journal of Language and Communication Disaorders , 38 (2), 131-148.
De La Paz, S. (1999). Composing via dictation and speech recognition systems: Compensatory technology for students with learning disabilities. Learning Disabilities Quarterly , 22 (3), 173-182.
Drexel University, Office of Computing Services. (1995). Giving voice to student writing: exploring the uses of speech recognition and speech synthesis in a writing curriculum.final report. Drexel University, Office of Computing Services. Philadelphia: Drexel University.
Education Development Center, Inc. (1999, November 18). Spotlight on speech recognition. Retrieved March 13, 2008, from Speaking to Write: http://www.edc.org/spk2wrt/VR_HTMLdoc.html
Education Development Center, Inc. (1999). The use of speech recognition by students and in schools: an overview. Retrieved March 13, 2008, from Speaking to Write: http://www.edc.org/spk2wrt/resources/spintr.html
Education Development Center, Inc. (1999). Understanding the use of continuous speech recognition software for writing. Retrieved March 13, 2008, from Speaking to Write: http://www.edc.org/spk2wrt/resources/ucsr.html
Follansbee & Speech Recognition and Secondary Students with Disabilities. (1999, June 21). Student characteristics to evaluate when considering use of speech recognition. Retrieved March 2008, from Speaking to Write: http://www.edc.org/spk2wrt
Follansbee. (2002, February). Update on contininuous speech. Retrieved March 13, 2008, from Speaking to Write: http://www.edc.org/spk2wrt/contsp.html
Forgrave, K. E. (2002). Assistive technology: Empowering students with learning disabilities. Assistive Technology , 75 (3), 122-126.
Gajar, A. (1998). Postsecondary Education. In F. Rusch, & J. Chadsey (Eds.), Beyond high school: transition from school to work (pp. 383-405). Belmont, CA: Wadworth.
Gould, J. D. (1978). How experts dictate. Journal of experimental psychology: human perception and performance , 4, 648-651.
Graham, S. (1999). The role of text production skills in writing development: A special issue. Learning Disabilities Quarterly , 22 (2), 75-77.
Higgins, E. L., & Raskind, M. H. (2000). Speaking to read: The effects of continous vs. discrete speech recognition systems on the reading and spelling of children with learning disabilities. Journal of Special Education Technology , 15 (1), 19-30.
Higgins, E., & Zvi, J. (1995). Assistive technology for post-secondary students with learning disabilities. Annals of Dyslexia , 45, 123-142.
Honeycutt, L. (2003). Researching the use of voice recognition writing software. Computers and composition, 20 (1), 77-95.
Kroll, B. M. (1981). Developmental relationships between speaking and writing. In B. M. Kroll, & R. J. Vann (Eds.), Exploring speaking-writing relationships: connections and contrasts (pp. 32-54). Urbana, IL, USA: NCTE.
Litten, M. (1999). Introducing voice recognition software to dyslexic users in a specialist school. Dyslexia , 5, 118-122.
Lowe, C. (2001). Speech recognition: sci fi or composition? Retrieved June 21, 2001, from Currents in Electronic Literacy,: http://www.cwrl.utexas.edu/spr01/lowe.html
McCutchen, D. (1987). Childrens’ discourse skills: form and modality requirements of school writing. Discourse processes , 10, 267-286.
Murray, C., Goldstein, D. E., Nourse, S., & Edgar, E. (2000). The postsecondary school attendance and completion rates of high school graduates with learning disabilities. Learning Disabilies research , 15, 119-127.
National Center To Improve Practice (NCIP). (2003, February 6). Spotlight on voice recognition. Retrieved March 7, 2008, from rehabtool.com: http://www.rehabtool.com/forum/discussions/96.html
National Center to Improve Practice. (2003, February 6). Use of voice recognition in special education. Retrieved March 7, 2008, from http://www.rehabtool.com: http://www.rehabtool.com/forum/discussion/97.html
Nicholson, R. I., & Fawcett, A. J. (1990). Automaticity: A new framework for dyslexia research? Cognition , 30, 159-182.
Reece, J. E., & Cumming, G. (1996). Evaluating speech-based composition methods: planning dictation and the listening word processor. In C. M. Levy (Ed.), The science of writing: Theories, methods, individual differences and applications (pp. 361-380). Mahwah, NJ, USA: Erlbaum.
Roberts, K. D., & Stodden, R. A. (2005). The use of voice recognition software as a compensatory strategy for postsecondary education students receiving services under the category of learning disabled. Journal of vocational rehabilitation , 22, 49-64.
Ruley, C. D. (1994). Do what i say! voice recognition makes major advances. Technos; Quarterly for education & technology , 3 (2), 15-17.
Sanderson, A. (1999). Voice recognition software. a panacea for dyslexsic learners or a frustrating hindrance? Dyslexia , 5, 113-118.
Schneiderman, B. (2000). The limits of speech recognition. Communications of the ACM , 43 (9), 63-65.
Vogel, S., & Adelman, P. (1992). The success of college students with learning disabilities: factors related to educational attainment. Journal of Learning Disabilities, 25, 430-441.
Vogel, S., & Moran, M. (1985). Written language disorder in learning disabled college students: A preliminary report. In W. Cruickshank, & J. Lerner (Eds.), Coming of age: The best of ACLD (pp. 212-225). Syracuse, NY: Syracuse University Press.
Zoellner, R. (1969). Talk-write: A behavioral pedagogy for composition. College English , 30 (4), 267-320.