How Does Speech Recognition Work: Behind The Scenes Using AI

Speech recognition is becoming a popular “must have” feature. It has been around for over 50 years and developed by several companies in the United States, Europe, Japan and China. But what people don’t know is that it also requires a lot of work behind the scenes to make speech recognition possible as well as practical.

Table of Contents

• What is speech recognition?

• How does speech recognition work?

• What are the benefits of speech recognition?

• What are the challenges of speech recognition?

• How can speech recognition be used in artificial intelligence?

• Testing your speech model

What is speech recognition?

Speech recognition is the process of translating human speech into a written format. Speech recognition technology is used in a wide variety of industries today. It is commonly confused with voice recognition. However, speech recognition technology has improved steadily over the years and it is now used to understand and process human speech.

Speech recognition technology has improved rapidly in recent years due to advancements in deep learning and big data. Advanced speech recognition solutions use AI and machine learning to understand and process human speech. Speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning and integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, speech recognition applications and devices learn as they go, evolving responses with each interaction.

Speech recognition can be customized for different purposes, such as language weighting and speaker labeling. Acoustics can be trained to improve accuracy. Speech recognition can be used in many different business scenarios where companies are making in roads in several areas of speech recognition.

Tip:

To properly train speech recgognition systems, one needs a large amount of speech recordings with high diversity. You can get these various voice datasets from the crowd via clickworker.

More about Voice Datasets

How does speech recognition work?

Language and acoustic modeling is the method via which speech recognition employs algorithms. The link between audio impulses and linguistic components of speech is represented via acoustic modeling. Language modeling, on the other hand, pairs word sequences with sounds to help separate similar-sounding words or phrases. Additionally, Hidden Markov Models, or HMMs, are frequently utilized to identify specific temporal speech patterns and thereby boost system accuracy. An HMM is a statistical model that depicts a system that evolves at random, with the assumption that changes in the future are independent of changes in the past.

The usage of N-grams with natural language processing is another technique for speech recognition. The complete speech recognition process is made simpler and takes less time to implement thanks to natural language processing, or NLP. N-grams, on the other hand, offer a more straightforward approach to language models and function by generating a probability distribution for a specific sequence. Finally, cutting-edge AI and machine learning technology will be included into the most sophisticated speech recognition software.

What are the benefits of speech recognition?

The benefits of speech recognition is an endlessly growing list, therefore contributing immensely to its popularity. The benefits mentioned below are the reason why speech recognition is a growing field in today’s day and age, and why everyone is keen on knowing how speech recognition works.

1. Benefits of speech recognition include faster operations, improved accuracy, and increased efficiency.

Speech recognition software is designed to be faster and more accurate than human beings. This means that it can be used to automate business processes and provide instant insights into what is happening in phone calls. The technology is also more accurate than a human and costs less per minute. Additionally, speech recognition software is readily accessible and easy to use.

2. Speech recognition can help reduce errors, improve customer satisfaction, and speed up processes.

Speech recognition technology can help reduce errors, improve customer satisfaction, and speed up processes in a variety of industries. In healthcare settings, speech recognition is used to capture and log patient diagnoses and treatment notes. This can help reduce customer wait times and improve satisfaction. In call centers, speech recognition can be used to transcribe phone calls quickly and accurately. This can save time and improve the efficiency of the call center. Speech recognition can also be used as part of security protocols to resolve issues for customers more quickly. Overall, speech recognition technology can help reduce errors, improve customer satisfaction, and speed up processes.

3. In addition, speech recognition can help you create a more efficient and effective work environment.

Speech recognition software is more accurate and faster than a human, meaning it’s more cost-effective than using a human. In addition, speech recognition can be used to automate business processes and provide instant insights into call activity. This technology is also more accurate and efficient than human transcription.

What are the challenges of speech recognition?

Though speech recognition comes with a lot of benefits and applications, there are quite a few challenges also present due to the complexity of this software.

1. The lack of standardization of speech

The lack of standardization in speech creates challenges for speech recognition because different people speak differently depending on their region, age, gender, and native language. Developers of speech recognition tools should take this into account and publicly report their progress to help ensure a equitable development process.

2. The different accents and pronunciations of words

Different accents and pronunciations can impact speech recognition technology in a number of ways. First, different accents can make it difficult for the software to understand what is being said. This is because the software is programmed to recognize certain sounds and patterns associated with specific words. When someone speaks with a different accent, those sound patterns can be altered, making it more difficult for the software to correctly identify the word.

Second, different dialects of a language can also impact speech recognition accuracy. This is because each dialect has its own unique way of pronouncing words and phrases. When speech recognition software is not programmed to account for these differences, it can lead to errors in recognition.

Finally, research has shown that accent and pronunciation can also affect accuracy rates for individual users. Speech recognition technology may be less effective for people who speak with an accent or dialect that is not well-represented in the data used to create the software.

3. The different speeds of speech

Speech recognition is the process of converting spoken words into text. It is a complex task for machines, as it can be affected by many factors, such as background noise, echoes, and different speeds of speech. The accuracy of speech recognition varies depending on these factors. For example, different speeds of speech can impact the accuracy of speech recognition. If a person speaks too quickly, the machine may not be able to understand all the words that are spoken. If a person speaks too slowly, the machine may have difficulty understanding the structure of the sentence. The accuracy of speech recognition also increases with vocabulary size and speaker independence. Therefore, different speeds of speech can impact speech recognition in terms of accuracy and processing speed.

4. The different noise levels in different environments

Speech recognition technology is complex, and it is still accurate even in noisy environments. However, noise levels can impact speech recognition accuracy. Background noise can easily throw a speech recognition device off track. Engineers have to program the device to filter out ambient noises and turn them into text that the software can understand. Recording tools can also have a significant impact on speech recognition accuracy. Customized data collection projects are often needed to overcome recording challenges. Voiceover artists can be recruited to record specific phrases or in-field collection can be used to collect speech in a more real-world scenario.

5. The different types of speech

Different types of speech can have an impact on speech recognition accuracy. For example, pronunciation can be a factor, as well as the type of speech (monotonic, disordered, etc.). Additionally, the complexity of the sound signal can impact accuracy.

One way to improve recognition accuracy is by taking into consideration the different types of speech and making decisions probabilistically at lower levels. This allows for more deterministic decisions to be made only at the highest level. Another way to improve accuracy is by expanding the complexity of sounds through neural networks.

6. The different context in which speech is used

The context in which speech is used can impact the accuracy of speech recognition. Speech recognition accuracy is often impaired in spontaneous speech compared to when it is read aloud. This is because the machine checks for simpler, more probabilistic rules when recognizing sounds. To increase speech recognition accuracy, we need to take into consideration neural networks.

7. The different purposes of speech

The different purposes of speech affect speech recognition in a few ways. First, well-designed speech recognition software is easy to use and often runs in the background. Second, speech recognition software that incorporates AI becomes more effective over time as it accumulates data about human speech. Finally, the different purposes of speech can affect the accuracy of the software. For example, if someone is speaking to entertain, they may use more slang or talk faster, which can make it harder for the software to understand.

How can speech recognition be used in artificial intelligence?

The use of virtual personal assistants and speech recognition technology has fast spread from our cellphones to our homes, and its applications in sectors including business, finance, marketing, and healthcare are starting to become clearer.

AI for speech recognition in communications

The largest benefit that speech recognition technology can offer the telecommunications sector is around conversational AI, like it does for many other sectors. These voice recognition systems enhance and add value to currently available telecommunication services because they can detect and engage in casual conversation and increasingly understand human speech. Additionally, it helps to strengthen targeted marketing initiatives, enable self-service, and better the entire customer experience.

The time it takes for customers to find what they need is reduced, and frequently they may sign up for new services or add-ons without even speaking to a human. All of the above are made easier with the use of self-service virtual assistants that are driven by speech recognition technology.

AI for speech recognition in banking

Security and customer experience are currently top objectives for customers in banking. Both can benefit from the application of AI in banking, especially speech recognition systems.

Many institutions use speech recognition to facilitate payments in mobile and online banking from a security standpoint. A common use case for voice authentication in mobile banking applications is to provide consumers with a simple means of identity verification in addition to complex passwords and 2-factor authentication procedures without the usual headache.

From the perspective of customer service, utilizing speech recognition to do mobile banking and handle customer service issues results in a simplified procedure because customers don’t have to wait in long service or support queues to speak to human agents for very simple resolutions.

AI for speech recognition in healthcare sector

For healthcare professionals to spend less time on data entry and more time treating patients, speech recognition has become a crucial tool. It has made it easier to remotely check for symptoms, provide patients with vital information during times of great perplexity, and generally lessen the exposure of healthcare professionals while still enabling them to give their patients the care they need. Speech recognition has already contributed much to remote healthcare and will only become better.

Minimizing the amount of time spent on administrative tasks related to electronic health records, relieving some of the doctors’ workload related to time spent at the computer inputting data and allowing them to concentrate on the patient are one of the applications of AI. AI will improve its comprehension of common and medical vocabulary, speaking patterns, etc. as speech recognition technology becomes more specialized. This will open the door for more sophisticated note-taking that will require less data entry while still recording important patient information.

Testing your speech model

The most crucial component of an effective speech recognition system is high-quality data, as you the output solely depends on the input. Therefore, the next step in ensuring that your system is ready to operate to its highest potential is choosing the appropriate training data.

Where can I find data on speech recognition?

In today’s world, data is now contextualized with the process and the agents who contributed to it rather than being inaccessible.

In order to maximize diversity and train models that speak to everyone, everywhere, known contributors can be actively sought. Or to put it another way, we can gather and evaluate audio datasets with a wide range of demographics by leveraging a varied population.

Robert Koch