While the world is still exploring the beneficial effects of artificial intelligence (AI) and machine learning algorithms, speech recognition is considered to be one of the pillars for some time now. Machine learning has been studied extensively in optimizing speech recognition for a variety of applications. Teaching and learning, auto-dictation, speech to text transcription and YouTube closed caption, voice control, constitute just a small set of examples for speech recognition applications that we use seamlessly in our daily life, without even thinking how a computer algorithm can accurately recognize the words that we utter and act accordingly. Although speech recognition has numerous benefits, it still suffers from many drawbacks mainly in terms of security concerns and accuracy.
Languages and the learning involved therein is a relatively challenging task given the skills that need to be developed to reach the intended targets. Traditional learning techniques take a long time to complete, are considerably costly, and are not open to everyone. In this context, speech recognition provides a myriad of opportunities and advantages that go beyond traditional learning outcomes.
What is Speech Recognition?
As its name stands, speech recognition algorithms try to explore the properties of spoken words, identify them, and use them in the required context. A good speech recognition algorithm is one that has the least number of errors in the recognized words. Speech recognition is not simple as people even with the same mother tongue, utter words differently with many dialects. Some dialects are not understood by human beings, so you can imagine the tough task of automatically recognizing words by a computer algorithm.
This said, many factors contribute to the success of speech recognition. This starts with proper training and feature extraction. Extracting “the most useful features” is essential is accurately distinguishing words from each other. The AI/machine learning tools used is another important step. These tools need to properly separate the words from each other. Natural language processing (NLP) has even been widely used recently to improve word recognition and extend it to complex sentences and structures.
How Can Speech Recognition Be Used to Teach Languages?
Whether it is basic grammar or vocabulary, pronunciation or even developing oral conversational skills, speech recognition is one of the most efficient tools to achieve these tasks. Instead of costly courses which usually span multiple levels, each with a dedicated booklet; people are now resorting to mobile apps where they can navigate different levels of difficulty and experiment with various types of exercises, all embedded in a friendly user interface. Not only this, learners can save their progress and obtain detailed statistics concerning areas that are lacking and even suggestions on how to improve their learning process.
Speech recognition and the advanced analytics provided by the underlying machine learning algorithms are capable of providing these features which replace costly physical classes. Speech recognition provides a way of mastering the three components of any language, namely reading, writing and oral conversational skills. With apps developed around speech recognition algorithms, learning languages is no longer a burden, but rather an enjoyable experience where a user can pace his efforts according to his retention capabilities. With this, the embarrassing situation where an individual in difficulty compares himself to his astute peers is gone. Moreover, the learning process is decoupled from the grading that is used in schools which reduces the accompanying stress.
Breaking the Disability Barrier
The use of speech recognition for learning presents itself as a hope, a hope for people who are sidelined because of their disabilities.
People with limited motor skills or even with motor neuron diseases can resort to speech recognition to reintegrate the learning cycle, akin to their peers. The inherent issues that prevented them from writing, doing assignments and exams, can be overcome with apps that provide automatic recognition and transcription.
Individuals that suffer from problems that cause literacy problems such as dyslexia, dysgraphia, and dyspraxia can feel more confident in the learning process. Writing difficulties can be easily overcome with automated word recognition. Speaking difficulties or even problems forming sentences can be ironed out with specific advanced features of speech processing especially if coupled with natural language processing. Advanced algorithms can be used to guess the words the speaker is intending to use, and even correct the sentence by trying to find the most plausible sequence of words.
Visually impaired individuals can gain an increasing autonomy interacting with the computer/mobile interface and having feedback as they progress.
Speech Recognition for Educational Equality
The United Nations has dedicated a sustainable development goal (SDG) for education. SDG 4 aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”. Speech recognition apps do not discriminate between boys and girls, poor and rich, disabled or not. AI including speech recognition algorithms, are bringing schools into the digital age by allowing the development of complete curricula, notably in terms of language development, which can be tailored to different languages and their properties and to the targeted learners and their capabilities. Whether it is English, Arabic, or even Mandarin, the learning module can be developed differently. The speech recognition algorithm used is also different for each of these languages given their largely differing properties.
Challenges of Speech Recognition in Learning Languages
As any other machine learning product, speech recognition is prone to mistakes as a 100 percent accuracy cannot be guaranteed. Relying solely on the computer output may render the learning experience flawed. Proper guidelines should be given to ensure that the learner is alert that the recognition app can provide erroneous output or even fail to recognize wrong input from the users themselves. What generally applies to technology is more critical when it comes to learning languages as the user will be employing the acquired skillset in his daily life and interactions with other people.
Learning languages is a crucial component of any educational system. While this normally a tedious process that requires a large number of costly modules or even span several years in the school systems, speech recognition can make the learning process available to everyone at minimal or no cost, and without the temporal or physical constraints of normal language learning techniques. Speech recognition also reduces inequalities in education providing the same quality for everyone irrespective of gender, age, or disability. As with any technology, the output of speech recognition-based apps should always be under strict scrutiny to ensure that the learning experience is not flawed.
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