Dragon Dictate: Difference between revisions
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==Key Innovations== | ==Key Innovations== | ||
=== 1. '''Commercialize Continuous Speech Recognition | === 1. '''Commercialize Continuous Speech Recognition''' === | ||
Dragon Systems was one of the first companies to successfully commercialize speech recognition technology for personal computer users. They made it accessible to a broader audience beyond research and specialized industries.<ref>[https://www.dragon-medical-transcription.com/history_speech_recognition.html History of Speech & Voice Recognition and Transcription Software]</ref> | Dragon Systems was one of the first companies to successfully commercialize speech recognition technology for personal computer users. They made it accessible to a broader audience beyond research and specialized industries.<ref>[https://www.dragon-medical-transcription.com/history_speech_recognition.html History of Speech & Voice Recognition and Transcription Software]</ref> | ||
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In 1990, Dragon demonstrated a 5,000-word continuous speech system for PCs and introduced Dragon Dictate 30K, the first large-vocabulary, speech-to-text system for general-purpose dictation. It allowed control of a PC using voice commands only and found acceptance among the disabled. The system had limited appeal in the broader marketplace because it required users to pause between words. And late in 1993, the Bakers realized that improvements in desktop computers would soon allow continuous voice recognition. By July 1997, Dragon had launched Dragon NaturallySpeaking, a continuous speech & voice recognition program for general-purpose use with a vocabulary of 23,000 words. The package won rave reviews and numerous awards. | In 1990, Dragon demonstrated a 5,000-word continuous speech system for PCs and introduced Dragon Dictate 30K, the first large-vocabulary, speech-to-text system for general-purpose dictation. It allowed control of a PC using voice commands only and found acceptance among the disabled. The system had limited appeal in the broader marketplace because it required users to pause between words. And late in 1993, the Bakers realized that improvements in desktop computers would soon allow continuous voice recognition. By July 1997, Dragon had launched Dragon NaturallySpeaking, a continuous speech & voice recognition program for general-purpose use with a vocabulary of 23,000 words. The package won rave reviews and numerous awards. | ||
=== 2. '''Improved Accuracy | === 2. '''Improved Accuracy''' === | ||
The Dragon Dictate’ acoustic adaptability is based in a three-level phonology and a stochastic model of production:<ref>[https://doi.org/10.1177/002383099203500218 Mandel, M. A. (1992). A Commercial Large-Vocabulary Discrete Speech Recognition System: DragonDictate. Language and Speech, 35(1–2), 237–246.] </ref> | The Dragon Dictate’ acoustic adaptability is based in a three-level phonology and a stochastic model of production:<ref>[https://doi.org/10.1177/002383099203500218 Mandel, M. A. (1992). A Commercial Large-Vocabulary Discrete Speech Recognition System: DragonDictate. Language and Speech, 35(1–2), 237–246.] </ref> | ||
==== (1) Three-level Phonology | ==== (1) Three-level Phonology ==== | ||
This three-level approach alIows Dragon Dictate to adapt quickly to the user’s own speech, approaching the reference speaker’s performance within a few thousand words. | This three-level approach alIows Dragon Dictate to adapt quickly to the user’s own speech, approaching the reference speaker’s performance within a few thousand words. | ||
===== 1. Phonemes | ===== 1. Phonemes ===== | ||
It comprises 24 consonants, three unstressable syllabic resonants, and 17 vowels, including diphthongs and syllabic /r/. | It comprises 24 consonants, three unstressable syllabic resonants, and 17 vowels, including diphthongs and syllabic /r/. | ||
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# the degree of prepausal lengthening of each phoneme due to its position in the word. | # the degree of prepausal lengthening of each phoneme due to its position in the word. | ||
===== 3. Phonetics elements(PELs) | ===== 3. Phonetics elements(PELs) ===== | ||
Most instances of English /s/ have a lot in common acoustically, and even a highly context-dependent consonant like /t/ can show significant constancy. We implement this similarity as a slice of a very coarse spectrogram, a steady state called a phonetic element, or PEL. | Most instances of English /s/ have a lot in common acoustically, and even a highly context-dependent consonant like /t/ can show significant constancy. We implement this similarity as a slice of a very coarse spectrogram, a steady state called a phonetic element, or PEL. | ||
==== (2) A stochastic model of production | ==== (2) A stochastic model of production ==== | ||
It represents pronunciations as hidden Markov models that are built from the speech of a reference speaker according to three-level phonology, which in turn have been restructured into phonemes-in-context (PICs) and segments of PICs . | It represents pronunciations as hidden Markov models that are built from the speech of a reference speaker according to three-level phonology, which in turn have been restructured into phonemes-in-context (PICs) and segments of PICs . | ||
Performance is also improved by use of digram language modeling. Experience with labeling has led to several observations on Germanic resonants and on polyphthongs. | Performance is also improved by use of digram language modeling. Experience with labeling has led to several observations on Germanic resonants and on polyphthongs. | ||
==== (3) Adaptation | ==== (3) Adaptation ==== | ||
When a person starts using Dragon Dictate it makes a model of his or her speech: a user model. Initially this is just a copy of its base model, which is derived from the reference speaker’s voice, but with every word spoken it adapts to the user’s pronunciation and usage. | When a person starts using Dragon Dictate it makes a model of his or her speech: a user model. Initially this is just a copy of its base model, which is derived from the reference speaker’s voice, but with every word spoken it adapts to the user’s pronunciation and usage. | ||
For example, Initial recognition performance, which is significantly poorer when the user is of opposite sex to the reference speaker, is improved by adapting the models in advance to a speaker of the user’s own sex through preadaptation. | For example, Initial recognition performance, which is significantly poorer when the user is of opposite sex to the reference speaker, is improved by adapting the models in advance to a speaker of the user’s own sex through preadaptation. | ||
=== 3. '''Accessibility | === 3. '''Accessibility''' === | ||
Dragon Systems recognised the potential of speech recognition technology in enhancing accessibility for individuals with disabilities. Their software provided a means for people with mobility or dexterity challenges to interact with computers and create content independently. | Dragon Systems recognised the potential of speech recognition technology in enhancing accessibility for individuals with disabilities. Their software provided a means for people with mobility or dexterity challenges to interact with computers and create content independently. | ||
Revision as of 08:05, 17 September 2023
Group Members
Lin Introduction
Erin Historical Context
Eva Key Innovations
Chenyi Impact
Soogyeong Future Research
Introduction
1990 - The company, Dragon came out with the software, Dragon Dictate, which was a dictation software that could recognize human speech and dictate it into a word processing program.[1] It is also one of the first commercially successful speech recognition software packages for personal computers, making speech recognition more accessible. In 1997, Dragon NaturallySpeaking was released at the World Trade Center, becoming the world’s first continuous speech dictation system for consumers.[2]
Automatic Speech Recognition (ASR)
To better understand dragon dictate, we first need to understand what automatic speech recognition technology is. Automatic Speech Recognition (ASR) is a “machine-based process of decoding and transcribingoral speech”(Levis & Suvorov, 2012, p. l) that is built into numerous technologies such as automated call centers and dictation programs.
Much of the early interest in ASR focused on dictation programs. Dictation programs were developed for native speakers of a given language and are built into both Windows and Mac operating systems as part of their accessibility services. Dictation programs use ASR to interpret what the user has said and provide the spoken utterance in written form.[3]
Dragon Dictate's Development Steps
As one of the first commercially successful speech recognition software packages for personal computers, Dragon Dictate used discrete speech where the user must pause between speaking each word. The first version, 1.0 was available only through a few distribution and support partners. It included a Shure cardioid microphone headset.
Later it was replaced by Dragon NaturallySpeaking, which allows continuous speech recognition and correction and training of words via the keyboard.[4]
Now Dragon Dictate has more and more comprehensive features, that enable users to use their voice to interact with their own computers. Specifically, we can use Dragon Dictate to do the following things:
- Dictate text to be automatically typed into a document.
- Use verbal commands to edit text.
- Speak application commands to control computer operations.
- Use verbal commands to manipulate the pointer to perform commands.[5]
In order to help you better understand dragon dictate, in this wiki, we will elaborate on its historical context, key innovations, impact, and future research.
Historical Context
The exploration of automatic speech recognition and transcription commenced in 1936 at AT&T's Bell Labs. During this period, most research was funded and conducted by universities and governmental entities in the United States, notably the military and DARPA (Defense Advanced Research Project Agency). Commercial adoption of this technology did not happen until the early 1980s. [6] The release of Dragon Dictate in 1990 was a turning point in the evolution of speech recognition technology. It is critical to take into account the technological status of the time as well as the larger socio-economic climate in order to provide historical context.
- Growth of personal computing: This time period corresponded with the increasing popularity of personal computers. Dragon Dictate was released to customers who owned computers, making voice recognition more accessible.
- Advancements in hardware: advancements in computer hardware during the late 1980s and early 1990s in terms of processor power and memory capacity allowed for more advanced voice recognition systems.
- Vocabulary restrictions: Dragon Dictate, like many early voice recognition systems, had a limited vocabulary when compared to contemporary systems. It performed well in dictation tasks but struggled to recognize a large variety of words and phrases.
Key Innovations
1. Commercialize Continuous Speech Recognition
Dragon Systems was one of the first companies to successfully commercialize speech recognition technology for personal computer users. They made it accessible to a broader audience beyond research and specialized industries.[7]
Before, Dragon Dictate uses discrete speech and is speaker-dependent, adapting to the speaker’s voice and language model with every word.
In 1990, Dragon demonstrated a 5,000-word continuous speech system for PCs and introduced Dragon Dictate 30K, the first large-vocabulary, speech-to-text system for general-purpose dictation. It allowed control of a PC using voice commands only and found acceptance among the disabled. The system had limited appeal in the broader marketplace because it required users to pause between words. And late in 1993, the Bakers realized that improvements in desktop computers would soon allow continuous voice recognition. By July 1997, Dragon had launched Dragon NaturallySpeaking, a continuous speech & voice recognition program for general-purpose use with a vocabulary of 23,000 words. The package won rave reviews and numerous awards.
2. Improved Accuracy
The Dragon Dictate’ acoustic adaptability is based in a three-level phonology and a stochastic model of production:[8]
(1) Three-level Phonology
This three-level approach alIows Dragon Dictate to adapt quickly to the user’s own speech, approaching the reference speaker’s performance within a few thousand words.
1. Phonemes
It comprises 24 consonants, three unstressable syllabic resonants, and 17 vowels, including diphthongs and syllabic /r/.
2. Phonemes in context (PICs): the fundamental unit of Dragon phonology
A PIC represents an augmented triphone, comprising:
- the phoneme before the one being modeled, as context
- the phoneme being modeled
- the phoneme after the one being modeled, as context
- the degree of prepausal lengthening of each phoneme due to its position in the word.
3. Phonetics elements(PELs)
Most instances of English /s/ have a lot in common acoustically, and even a highly context-dependent consonant like /t/ can show significant constancy. We implement this similarity as a slice of a very coarse spectrogram, a steady state called a phonetic element, or PEL.
(2) A stochastic model of production
It represents pronunciations as hidden Markov models that are built from the speech of a reference speaker according to three-level phonology, which in turn have been restructured into phonemes-in-context (PICs) and segments of PICs .
Performance is also improved by use of digram language modeling. Experience with labeling has led to several observations on Germanic resonants and on polyphthongs.
(3) Adaptation
When a person starts using Dragon Dictate it makes a model of his or her speech: a user model. Initially this is just a copy of its base model, which is derived from the reference speaker’s voice, but with every word spoken it adapts to the user’s pronunciation and usage.
For example, Initial recognition performance, which is significantly poorer when the user is of opposite sex to the reference speaker, is improved by adapting the models in advance to a speaker of the user’s own sex through preadaptation.
3. Accessibility
Dragon Systems recognised the potential of speech recognition technology in enhancing accessibility for individuals with disabilities. Their software provided a means for people with mobility or dexterity challenges to interact with computers and create content independently.
- Dragon Dictate, despite its learning curve and motivation requirements, has the potential to significantly benefit individuals dealing with profound dysarthria and substantial computer access challenges. According to the research findings, it proves to be a valuable tool with substantial utility, capable of enhancing computer access capabilities and facilitating communication for two individuals with cerebral palsy.[9]
- Dragon can also help those people with learning difficulties like dyslexia or other conditions where they find it challenging to express their ideas in writing. And it can help students with mental health conditions or anxiety related problems for making their tasks easier to relieve their stress.[10]
- Dragon Dictate introduced the concept of using voice commands to control a computer, open applications, navigate the web, and perform various tasks. This added a new dimension to hands-free computer interaction. Nuance[11], the company behind Dragon Dictate, is a major player in healthcare technology, offering speech recognition and documentation solutions tailored for healthcare professionals. Their technology allows doctors, nurses, and other healthcare providers to transcribe medical dictation and interact with electronic health records using voice commands to improve efficiency and bring more comfortable experience.
Impact
Future Research
LLMs
References
- ↑ 230906 Cerrato presentation --- Brightspace
- ↑ nuance
- ↑ ASR DICTATION PROGRAM ACCURACY: HAVE CURRENT PROGRAMS IMPROVED?
- ↑ [1]DragonDictate
- ↑ Dragon Dictate 2.5: Visual QuickStart Guide
- ↑ https://www.dragon-medical-transcription.com/history_speech_recognition.html
- ↑ History of Speech & Voice Recognition and Transcription Software
- ↑ Mandel, M. A. (1992). A Commercial Large-Vocabulary Discrete Speech Recognition System: DragonDictate. Language and Speech, 35(1–2), 237–246.
- ↑ Satsuki Nakai, David Beavan, Eleanor Lawson, Grégory Leplâtre, James M. Scobbie & Jane Stuart-Smith (2016): Viewing speech in action: speech articulation videos in the public domain that demonstrate the sounds of the International Phonetic Alphabet (IPA), Innovation in Language Learning and Teaching
- ↑ How Dragon speech recognition helps students with disabilities
- ↑ Microsoft + Nuance