An Edtech user’s vocabulary to speech recognition and class AI

In the middle Recent The White Paper, former Scholastic president of education, Margare Meyer, has called 2021 the “Year of Speech Recognition” in education. And she may be right: the increase in adoption by Adtech developers in the first half of this year reflects the recognition that technology has the potential not only to create a more engaging learning experience for students, but to completely transform the entire system of early literacy education. .

In previous years, such a vision may have seemed far-fetched. But as Edsurge noted earlier, the science behind speech recognition for children is beginning to age, enabling educational applications that have sparked Adtech’s interest. Developer, Teachers and researchers alike.

Part of enabling the growing use of voice recognition in education is the availability of technologies designed specifically to cater to children’s voices and behaviors today. The previous speech recognition system was based on the voice of adults and lacked the necessary accuracy in the academic context. Specific speech recognition for children that now powers oral reading fluency tools are more accurate and effective and have the potential of what I can offer Is described As an increased “feedback return” for children and their teachers.

These new voice-enabled learning tools also have the ability to address equity and bias. Speech recognition that empowers them is designed with diversity in mind so that all pronunciations and dialects can be understood equally – thus reducing democratic access to educational resources, and the risks of inherent bias, for example, in observational assessments. Perhaps most importantly, these solutions are “personal and authentic” because they are the most natural tool for students ’learning: using their own voice.

In education, 2021 may be the year of speech recognition, but for most teachers, families, and students, the technology itself is relatively new, even if they have a voice assistant or smart speaker at home. And, given the strength of this technology, I expect more solutions Amplify’s MClass Express In order to enter the market, it is important for teachers and others to understand how they work and how to use them.

Recently, I did Amelia of Soapbox Labs, the vice president of speech technology there, to create A dictionary To better educate teachers and AdTech developers with speech recognition and to make informed decisions about its use in educational settings. Below are some important terms with an explanation of why those terms are important.

Artificial Intelligence (AI)

A system specifically designed to carry out tasks autonomously rather than being programmed by humans.

Why is important: AI is increasingly being used in educational products, it is a trend, no doubt, it will continue in the coming years.

Machine learning

A subset of AI that trains computers on large amounts of data so that they can perform tasks automatically and largely.

Why is important: Machine learning algorithms “learn” and “improve” with each experience, which improves the speech recognition function of voice-enabled educational tools.

In-depth teaching

Machine learning algorithms based on deep neural networks, which require a large amount of training data, and a multi-layered architecture that allows them to model complex behaviors such as human speech and language usage.

Why is important: Neural networks are widely used for speech recognition, image recognition, and other pattern-recognition problems, with applications for K-12 learning.

Voice technology

An umbrella term for technology that allows users to interact with products, services and platforms using their voice. The basic technologies that enable this are speech recognition (understanding human speech), speech synthesis (computer speaking), natural language processing (reading and understanding human language) and machine translation (translating human speech from one language to another).

Why is important: In the context of K-12 edtech, voice technology आणि and speech recognition, in particular वापर can empower multiple cases of use, enabling independent reading practice, language learning, dyslexia screening, feedback learning, and summary and structural assessment.

Automatic speech recognition / speech recognition / speech-to-text

Allows digital devices to convert text into speech, making it easier for the device to understand the purpose of the speaker. Words or concepts in the text can trigger actions (e.g., “turn off the lights,” “send text to my sister”).

Why is important: Once a child’s reading is transcribed into a digital device, he can compare it to a rubric to determine reading flow and comprehension. It can also provide a time stamp for individual words, making it easier for the teacher to find specific words or phrases read by the child and listen to them back. These systems can return “confidence points” to pronunciation, words and even sound levels.

D-biasing

Deliberate procedures used to reduce or eliminate unexpected biases in speech recognition. Artificial intelligence systems can reflect the biases of their creators, resulting in inferior and often unfavorable experiences for underprivileged users. Machine learning algorithms, in particular, make decisions based on the data set on which they have been trained, and they can be biased if those data sets are not representative of different populations.

Why is important: A biased system can extend and propagate the deep-seated biases placed by the designers of that system, as well as the limits of available data sets. The consequences of such bias in practice, assessment and screening platforms and learning tools for children can be devastating. If a biased system fails to understand the child’s pronunciation or dialect while reading, for example, it may give the child back that they are poor readers, in fact, they are reading correctly. An impartial system, on the other hand, will provide education companies and platforms with easy and unbiased feedback and data to support their learning journey.

Voice enabled evaluation

Uses speech recognition technology to invisibly learn, recognize and comprehend while the child is reading aloud.

Why is important: Voice-enabled assessment tools used in the classroom and remotely can provide data on pronunciation and oral reading streams. They can be used to learn challenges like dyslexia. When used for robust assessment, speech recognition technology provides data that can support and improve educational outcomes for children, as well as help determine the type and level of support provided by teachers.

Keyword Search

Speech recognition engine feature that recognizes keywords and sentences in speech.

Why is important: Finding keywords is especially useful when analyzing children’s speech, where search terms in an audio file can be identified alone, in sentences, or by background sounds. For example, a child can choose his favorite animal from the list. Keyword detection can score for every possible response, triggering a response in a game or lesson.

Pronunciation evaluation

Evaluates the pronunciation quality of a word or sentence.

Why is important: Speech assessment is a tremendous tool to save time for teachers, especially when supporting personal observation assessments because they provide teachers with scores that compare to what the child actually said to the target, enabling teachers to better understand where students might be. Make. Is struggling and needs more support or attention.

Assessment of fluidity

Evaluates children’s oral reading flow.

Why is important: Another tool to save time for teachers. When a child reads an excerpt, the speech recognition system records and counts word replacement, omission, inclusion, and the number of appropriate words. This, in turn, becomes a measure of fluidity, sometimes expressed as “word per minute right” or “WCPM”.

Speech-therapy assessment

Voice-enabled evaluators who evaluate speech patterns and sentence structure.

Why is important: Speech recognition-power screening and practice tools can identify speech patterns that can point to speech development pathology that enables students to practice at home during speech therapy sessions, while also providing progress data to speech therapists.

Privacy by design

An approach to technology development, design, and processes that ensures that individual users’ data privacy rights are protected from the earliest stages to the end user experience. Privacy gives companies transparency through design when it comes to data handling, for example, a commitment to use the data they collect to improve their services and not for any commercial purposes such as reselling, profiling or advertising.

Why is important: When it comes to children’s data rights, privacy cannot be considered later or at a later stage. Privacy must be maintained at every level of infrastructure, data and processes, and must be part of the conduct and vision of a voice-enabled solution from the very beginning.

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