How Do Speech Recognition Systems Work: Behind The Scenes Using AI

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Robert Koch

I write about AI, SEO, Tech, and Innovation. Led by curiosity, I stay ahead of AI advancements. I aim for clarity and understand the necessity of change, taking guidance from Shaw: 'Progress is impossible without change,' and living by Welch's words: 'Change before you have to'.

how do speech recognition systems work

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

Table of Contents

What are speech recognition systems?

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.


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 do speech recognition systems 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.

Video explaining how does speech recognition work

What are the benefits of speech recognition systems?

The benefits of speech recognition systems are 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 systems work.

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 systems can increase efficiency, create happy customers and maintain good levels of accuracy.

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 systems?

Though speech recognition systems come 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.

Video on different accents around the world

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.

Voice Recognition Data

Voice recognition data comprises audio recordings collected from various sources, capturing spoken language or vocal utterances. This data serves as the foundation for training and developing voice recognition systems, enabling them to accurately interpret and transcribe human speech into text.

Voice recognition data usually comprise of conversations, speeches, or scripted dialogues. These recordings can encompass a diverse range of languages, accents, and speaking styles to ensure the robustness and adaptability of the voice recognition system.

Once obtained, the voice recognition data undergoes preprocessing, which involves tasks like noise reduction, speech segmentation, and feature extraction to enhance the quality and relevance of the audio samples. Subsequently, the processed data is used to train machine learning algorithms, deep neural networks, or other models capable of recognizing speech patterns and converting audio input into text output accurately.

Voice recognition data plays a pivotal role in the development of voice-controlled devices, virtual assistants, speech-to-text transcription systems, and various applications in industries such as telecommunications, automotive, healthcare, and consumer electronics. Its widespread use underscores the importance of high-quality, diverse datasets in advancing the capabilities of voice recognition technology.

Obstacles when Obtaining Voice Recognition Data

Companies aiming to gather voice recognition data face several challenges:
  • Data Privacy and Security Concerns: Collecting audio data raises privacy concerns as it involves recording individuals’ voices, potentially without their explicit consent. Ensuring compliance with data protection regulations such as GDPR or HIPAA is crucial to avoid legal issues and maintain trust with users.
  • Ethical Considerations: There are ethical considerations regarding the collection and use of voice data, particularly in terms of transparency, consent, and potential biases. Companies must establish ethical guidelines for data collection and usage to address concerns related to user consent, data anonymization, and fair treatment of individuals.
  • Data Quality and Diversity: Acquiring high-quality and diverse voice data can be challenging. Variability in accents, languages, speech styles, and environmental conditions must be accounted for to develop robust and inclusive voice recognition systems. Ensuring representation across demographics and contexts is essential to mitigate biases and improve system performance for all users.
  • Cost and Resource Constraints: Gathering large-scale voice datasets requires significant resources, including equipment, personnel, and infrastructure for data collection, storage, and processing. Companies must allocate sufficient budget and manpower to manage the entire data collection pipeline effectively.
  • User Trust and Adoption: Building user trust is critical for successful data collection efforts. Companies need to communicate transparently about their data collection practices, address privacy concerns, and provide clear benefits to users to encourage participation. Ensuring a positive user experience during data collection can foster trust and increase user adoption rates.
  • Navigating these challenges requires careful planning, adherence to ethical principles, and proactive measures to address privacy, security, and quality concerns throughout the data collection process.

    How can speech recognition systems be used in artificial intelligence?

    speech recognition systems in AI

    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 systems 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 systems 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 systems 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 systems?

    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.

    FAQs on Speech Recognition Systems and how it works

    What are speech recognition systems?

    Speech recognition is the process of converting human speech into written form. Speech recognition software now has a wide vocabulary and is used in a variety of industries.

    Advanced speech recognition solutions use AI and machine learning to understand and process human speech. These applications are able to learn as they go, and get better with each interaction. Speech recognition systems can be customized to recognize specific details about a person's voice, which helps to improve accuracy. Acoustics training can also be used to improve the quality of speech recognition by focusing on sound effects and voice environments. Speech recognition is used to understand and interpret human speech, and is constantly improving at a rapid pace.

    What is the history of speech recognition?

    Speech recognition technology has been around for a long time. The history of speech recognition technology can be traced back to the early 1900s. In the early days, research was focused on emulating the way the human brain processes and understands speech. This approach was later replaced by more statistical modeling techniques, like HMMs (Hidden Markov Models). HMMs were controversial in the early days, but they have since become the dominant speech recognition algorithm. Today, speech recognition technology is widely used across many industries, including finance and retail.

    What are the main components of a speech recognition system?

    A speech recognition system has three main components: the acoustic model, language model, and lexicon. The acoustic model is used to improve precision by weighting specific words that are spoken frequently. The language model helps the system to understand and process different types of spoken language. The lexicon is a database of words and phrases that the system can recognize such as voice recognition data

    What are the different types of speech recognition systems?

    There are three main types of speech recognition: automatic, visual, and robust.

    • Automatic speech recognition is the most common type and is usually accurate. However, it can struggle with accents or noise.
    • Visual speech recognition can identify objects and people more accurately than automatic speech recognition, but it can be slower.
    • Robust speech recognition can handle difficult accents and noise better than visual or automatic speech recognition, but it may be slower.

    What are some common applications of speech recognition?

    Speech recognition is a versatile technology that is being used in an increasing number of applications. Common applications include mobile devices, word processing programs, language instruction, customer service, healthcare records, disability assistance, court reporting, and hands-free communication. Speech recognition can save time and lives in a variety of industries. The technology is becoming more ubiquitous and integrated into our lives as it becomes more refined.

    What is the future of speech recognition systems?

    The future of speech recognition technology is focused on ensuring pilots can spend more time on the mission. The demand for speech to text and text to speech services is fuelled by the need to make content available in many different formats. The medical field is using speech recognition technology to update patients' records in real-time. Speech recognition technology is growing in popularity, especially among white-collar workers. The development of the IoT and big data are going to lead to even deeper uptake of speech recognition technology.

    How can I get started with speech recognition systems?

    If you want to start using speech recognition, you need to install the SpeechRecognition library. You can install it using pip or by downloading and extracting the source code. The library has support for several different engines and APIs. To get started with speech recognition, try out the different tools listed in the Requirements section.

    What are some common speech recognition software programs?

    Speech recognition software programs are used to help machines understand human speech. These programs often have features that customize the program to the user's needs, such as language weighting and acoustic training, which can improve accuracy and performance. Additionally, speech recognition software can be equipped with filters to identify profanity and other undesirable words. Some advanced speech recognition solutions use artificial intelligence (AI) and machine learning to better understand human speech. As speech recognition technology advances, it is becoming more sophisticated in its ability to understand the complexities of human conversation.