Speech is the most natural form of human communication. Thanks to deep learning, we now know how to build speech recognition software systems that can process audio data and understand spoken language accurately.
In this post, I, Oleh Komenchuk – Machine Learning Engineer at Uptech – will explain what speech recognition is and how it works, as well as provide insights on how to build a robust speech recognition system. Whether you're just curious about the technology or looking for ways to implement it in your projects, you'll find everything you need to get started.
What is speech recognition?
Speech recognition, commonly referred to as speech-to-text or computer speech recognition, is the ability of machines to identify and transcribe human spoken language into written text. Systems based on such a technology move far beyond traditional text-based interfaces to more natural voice-driven communication. As such, they allow for better human-machine interaction.
At its core, speech recognition systems utilize complex algorithms and machine learning models to process and interpret audio inputs in real-time. These systems are designed to understand the varieties of human speech, such as
- different accents,
- intonations,
- speaking speeds,
- the use of informal language or slang, etc.
By analyzing these nuances, speech recognition technology can accurately convert spoken words into text or execute specific commands based on the recognized speech.
Speech recognition vs voice recognition
Voice recognition and speech recognition may be often used interchangeably, but they serve different purposes and have unique applications. In a nutshell, voice recognition involves technology that recognizes the voice of the speaker, while speech recognition is the technology that recognizes the actual words despite who the speaker is.
Below, we explore the core differences between the two.
Speech recognition
Speech recognition is the technology that can accurately identify and transcribe words spoken by a person into a comprehensible text format, irrespective of the speaker's identity. The focus of this technology is solely on understanding what is being said rather than on identifying who is speaking.
Advanced speech recognition systems use Automatic Speech Recognition (ASR) to transcribe human speech. It’s a versatile tool for numerous applications, such as:
- Transcribing voicemails into text for easy access.
- Allowing individuals who cannot type to communicate with computers, such as writing emails, conducting online searches, or completing assignments.
- Enabling voice commands to control devices or software.
Voice recognition
Voice recognition, on the other hand, identifies who is speaking based on the unique characteristics of their voice. Often called voiceprint recognition, this technology analyzes distinct vocal patterns, such as pitch, tone, and pronunciation, to authenticate or verify an individual’s identity. Voice recognition is primarily used in situations where security and personalization are paramount, such as:
- Voice biometrics for authentication, such as in banking systems where the user’s voice is their password.
- Access control for devices or applications, ensuring that only recognized users can access specific features or information.
Reasons why you may need a speech recognition system
As a business owner, you can use speech recognition systems in a variety of ways, and they offer quite a few benefits to businesses of all types and sizes.
Better productivity
Speech recognition saves time by converting spoken words into text. As a result, you can do things like documenting faster by reducing manual data entry. Whether you work in healthcare, law, or other fields, you can quickly create records and notes, allowing them to focus on more valuable tasks.
Improved customer service
In customer support, speech recognition automates call transcriptions so that agents can focus on solving issues instead of taking notes. It also helps route calls and allows virtual assistants to handle routine questions, speeding up response times.
Accessibility for all
Voice control helps make technology accessible to individuals with disabilities as they can navigate software and control devices hands-free. This inclusivity benefits both employees and customers as it provides flexible ways to interact with the software your business relies on and reach a wider audience.
Cost savings and optimized operations
Automation of routine tasks with speech recognition reduces labor costs, minimizes errors, and speeds up workflows. This makes operations more efficient, which results in cost savings.
Increased user satisfaction
Employees and customers alike enjoy the convenience of voice-driven tasks, from hands-free work for employees to quick answers for customers. It adds a layer of comfort and personalization that improves the overall experience.
Versatility across industries
Speech recognition is adaptable to various industries – healthcare professionals can dictate patient information, retailers can offer voice-assisted shopping, and financial services can use it for secure verification and customer support, not to mention other domains.
7 Steps to Build a Speech Recognition System
With all the benefits listed, you’re probably thinking, “How exactly do I make a speech recognition system?” Well, this is a complex, multistage process that revolves around Automatic Speech Recognition (ASR) technology. ASR systematically extracts words and grammatical structures from audio signals and, as such, enables the system to understand and respond to spoken language. Generally, we stick to these 7 steps to make a speech recognition system:
- Define your goals and requirements
- Collect and prepare data
- Develop and train the AI models
- Test and evaluate the speech-to-text models
- Improve quality with post-processing
- Integrate and deploy a speech recognition model
- Provide ongoing support and updates
We will walk you through each step in more detail now.
Step 1. Define your goals and requirements
The first crucial step when it comes to building an AI sound recognition system is to define your objectives clearly. Determine the purpose of the system and what you aim to achieve with it.
For this, you need to:
- outline detailed requirements,
- estimate the number of features the system should have,
- calculate the budget necessary for development.
Speaking of the budget, a simple system, such as a Minimum Viable Product (MVP), typically requires 1-3 months of development and may cost between $10,000 and $50,000. A system of medium complexity might take 3-6 months, with costs ranging from $50,000 to $150,000. For a complex system, expect development times of 6+ months and costs exceeding $150,000.
Learn more about the general AI costs and more specific estimates of AI app development in our dedicated articles.
An additional factor to consider at this point is that data acquisition and preparation can add significantly to the overall expenses. Costs associated with data collection, annotation, and management are substantial and should be included in the budget.
To make things simpler and more understandable, we suggest that you opt for a discovery phase, as it can be highly beneficial.
During this phase, we help you refine your goals, identify potential challenges, and develop a roadmap for successful implementation.
Step 2. Collect and prepare data for an ASR model
Now, let’s move to data. The performance of a speech recognition system heavily depends on the quality and quantity of the data used to train it. If we want a neural net that can tell the nuances of speech, we’ll need speech data that has a lot of variations of gender, age, accent, etc.
Public datasets vs. building your own dataset
If such data isn't readily available, public datasets can serve as a starting point. For example, Common Voice is an open-source speech dataset initiative led by Mozilla, where each audio sample comes with corresponding transcribed text labels. However, publicly available datasets are often general-purpose and may not meet specific business requirements.
When specialized data is required, for example, to train or retrain a neural net for a narrowly focused use case or domain, it becomes necessary to collect your own audio recordings and transcriptions. At Uptech, one of our projects involved the transcription of pilots and air traffic controllers. So, we needed specialized audio data that wasn't publicly accessible in sufficient quantities to build an effective speech recognition system.
Data collection
The dataset should be substantial not only in quantity but also in quality. As with all AI and machine learning projects, the rule applies: the more data, the better, but data quality plays an equally important role. For example, if 90% of your dataset consists of noise and only 10% is actual speech, the model's performance will suffer significantly. That’s when the data preparation stage enters the game.
Data preparation
Data preparation means processing audio signals into formats suitable for training.
Audio signals can be represented as:
- Waveforms – vibrating lines that depict the recorded sound in terms of time and amplitude (loudness)
- Spectrograms – it has a third dimension – frequency– added to time and amplitude.
Like waveforms, spectrograms display time along the x-axis, but the y-axis represents the frequency spectrum, from low frequencies at the bottom to higher frequencies at the top. The loudness of a particular sound is indicated by the intensity or color in the spectrogram, with louder sounds appearing brighter.
When starting the development of a speech recognition system, we must consider several basic audio properties:
- Audio file format (e.g., MP3, WAV): Input devices record audio in different formats. While MP3 is commonly used, lossless formats like WAV or FLAC are preferred for higher quality.
- Sample rate (e.g., 8kHz, 16kHz, 24kHz): Determines how often the audio signal is sampled per second. A higher sample rate captures more detail in the sound.
- Number of channels (stereo or mono)
- Bitrate (e.g., 32 kbit/s, 128 kbit/s, 192 kbit/s)
- Duration of audio clips
Among these, audio file format and sample rate are the most critical. Sampling digitizes the sound by capturing its amplitude at discrete intervals, which is essential for the transformation of analog audio into a digital format that neural networks can process.
Building a data pipeline
A data pipeline transforms raw audio waves into waveforms or spectrograms. Processing character labels is also necessary if the model outputs characters instead of word probabilities. Decoding character probabilities is more efficient because it reduces the number of possible outputs (26 for the English language instead of thousands of words), making the model faster and more manageable.
Data augmentation techniques, such as SpecAugment, can make the dataset better as it artificially increases its diversity. SpecAugment masks sections of the spectrogram in both time and frequency domains and, as such, removes pieces of data. This forces the neural network to learn how to make correct predictions with imperfect data, improving its robustness and generalization to real-world scenarios.
Finally, data labeling or annotation comes in. It’s the transcription of the audio recordings over time, providing the labels necessary for supervised learning. Accurate annotation enables the model to distinguish sounds from noise and significantly improves its ability to recognize speech accurately.
Step 3. Develop and train the AI models for speech recognition
Humans are good at understanding speech. Well, at least the language we know. For computers, though, things are a lot more difficult. Speech recognition poses a challenging problem for machines because speech consists of sound waves with various physical properties influenced by numerous factors. A person's age, gender, lifestyle, accent, and personality traits all affect the way they speak, altering the physical characteristics of the sound. Additionally, environmental noise and the quality of recording equipment, such as microphones, introduce further variability.
These numerous variations and nuances in the physical properties of speech make it extremely difficult to come up with exhaustive rules for speech recognition. Moreover, beyond the physical properties, computers must also navigate the linguistic complexities of language.
Consider the sentence: "The bear ate the flower, too."
In this example, several words sound identical to other words with different meanings and spellings:
- "Bear" (the animal) sounds like "bare" (meaning uncovered).
- "Ate" (past tense of eat) sounds like "eight" (the number 8).
- "Flower" (a plant) sounds like "flour" (the baking ingredient).
- "Too" (meaning also) sounds like "two" (the number 2).
These homophones show how language can be full of nuances and variations, making it a complex task for computers to understand speech correctly. So, we use deep learning to deal with the task of speech recognition. Any modern speech recognition system today uses deep learning at some level.
To build an effective speech recognition model, you will have to deal with 2 tasks:
- Acoustic modeling
- Language modeling
Acoustic modeling
An acoustic model is designed to map audio signals to their corresponding textual representations. We need to build this first model to handle the variations and nuances inherent in speech – such as differences in age, gender, accents, recording equipment, and environmental conditions mentioned earlier.
At a high level, the acoustic model is a neural network that takes speech waveforms as input and outputs transcribed text. In order for our neural network to know how to properly do this, we’ll have to train it with a ton of speech data. Since speech is a sequential signal occurring over time, we need a model capable of processing sequential data.
Traditionally, experts used statistical approaches for acoustic modeling, such as:
- Hidden Markov Models (HMMs) – statistical models that represent speech as a sequence of hidden states with probabilistic transitions and observations. They capture the probabilities by moving from one state (e.g., a phoneme) to another.
- Conditional Random Fields (CRFs) – statistical models that calculate the conditional probability of a sequence of labels given a sequence of input data. This allows them to consider the context of neighboring labels for more accurate predictions.
However, neural networks have become the preferred method due to their superior ability to learn complex patterns in data. Common deep neural networks that can maintain context over sequences are:
- Recurrent Neural Networks (RNNs) – these neural networks use loops to maintain information across time steps and remember previous inputs in the sequence.
- Long Short-Term Memory (LSTM) networks are a special type of RNN that can learn long-term dependencies by using gating mechanisms to control the flow of information. As such, they overcome issues like the vanishing gradient problem and can retain information over longer periods.
- Convolutional Neural Networks (CNNs) are neural networks that use convolutional layers to capture local patterns through filters applied across input data; while originally designed for image processing, they can be adapted to process spectrograms in speech recognition, capturing temporal and spectral features.
- Transformers are advanced neural networks that use self-attention mechanisms to process sequences in parallel, capturing global dependencies without relying on sequential processing, which allows them to model relationships between all parts of the input sequence efficiently.
By the way, transformers are common for building generative AI systems, and you can get acquainted with the process by reading our dedicated article.
Acoustic modeling also encompasses pronunciation modeling, which describes how sequences of fundamental speech units (such as phonemes or phonetic features) represent larger units like words or phrases.
Language modeling
The second task addresses the linguistic aspects of speech. To inject linguistic knowledge into the transcriptions and resolve ambiguities, we need a language model in conjunction with a rescoring algorithm.
The output of the acoustic model is probabilistic; for each time step, it provides probabilities for possible words or phonemes. Naively selecting the highest probability at each step can lead to linguistically incorrect or nonsensical transcriptions, especially with homophones or words that sound similar.
For example, without a language model, the sentence could be misinterpreted due to similar-sounding words: "The bare eight the flour two."
To determine the most likely and grammatically correct sequence of words, the language model evaluates the probability of possible word sequences. It answers the question: What is the more likely sentence?
- Probability("The bear ate the flower too") = 0.95
- Probability("The bare eight the flour two") = 0.25
By building a probability distribution over sequences of words it's trained on, the language model helps the system choose the most coherent and contextually appropriate transcription.
Modern language models mostly use Transformers, which have demonstrated superior performance due to their ability to capture long-range dependencies in text. Transformers enable the model to consider the entire context of a sentence when predicting the next word, improving accuracy in transcriptions.
If a simpler solution is desired or resources are limited, rule-based language models like KenLM can be employed. While they may not capture complex linguistic patterns as effectively as Transformer-based models, they are less resource-intensive and can be suitable for certain applications.
A rescoring algorithm works alongside the language model to refine the hypotheses generated by the acoustic model. It reevaluates the initial predictions based on the probabilities provided by the language model, selecting the most likely and coherent transcription.
Step 4. Test and evaluate the speech-to-text models
After developing and training your models, we test and evaluate the speech recognition system's performance. For this, we normally measure the accuracy of the system by calculating the percentage of correctly recognized words versus those that are unrecognized or incorrectly transcribed.
Common evaluation metrics include the Word Error Rate (WER), which accounts for substitutions, deletions, and insertions needed to match the system's output to a reference transcription.
To conduct a thorough evaluation, we normally use a diverse test dataset that reflects real-world conditions, including various accents, dialects, background noises, and speaking styles. This helps identify the system's strengths and weaknesses across different scenarios.
If you have a speech recognition system that hasn’t been tested, we can help you make it better by thoroughly evaluating its performance and making improvements.
Step 5. Improve quality with post-processing
Based on the evaluation results, the next step is to refine the system through post-processing to improve its overall quality. For this, we can perform the following activities:
- Fine-tune hyperparameters or retrain the models with additional data to address identified shortcomings.
- Implement algorithms to filter out background noise and improve the clarity of the audio input.
- Apply text normalization to handle variations in speech, such as abbreviations, numbers, and colloquial expressions.
- Update the language model with more context-aware features and include domain-specific vocabulary to increase accuracy.
These post-processing steps help bridge the gap between the model's raw output and the desired performance level, ensuring the system delivers reliable and accurate transcriptions.
Step 6. Integrate and deploy a speech recognition model
With a refined and tested speech recognition system, the next phase is integration and deployment. So, what do we usually do at this point?
System integration
The first thing we do is check the system integration. The speech recognition system should seamlessly integrate with your existing applications or platforms. To do so, we set up APIs or interfaces that enable different software components to communicate with each other.
We have an informative post on how to integrate generative AI so you can completely understand it.
Infrastructure setup
Next, we configure the necessary hardware and software infrastructure, whether it's on-premises servers, cloud-based solutions, or a hybrid setup, to support the system's operational requirements.
Scalability and performance optimization
Then, the system needs to be optimized to handle varying loads and ensure low-latency responses. At this point, we do load balancing, efficient resource allocation, and performance tuning.
Security measures
Of course, we don’t forget about the implementation of security protocols to protect data privacy and prevent unauthorized access. We put a special focus on this if the system handles sensitive information, e.g., in domains like healthcare or financial services.
Our dedicated article details the top 12 best practices for mobile app security that we adhere to in our projects. Check it out.
User interface design
What we’ve learned from years of software development is that designing user-friendly interfaces that allow end-users to interact with the speech recognition system effectively is a huge must.
So why do we care so much about integration and deployment? Because these processes define whether the system can function efficiently in a real-world environment and provide value to its users.
Step 7. Provide ongoing support and updates
As with any software solution, building a speech recognition system is an ongoing process that doesn't end with deployment. Continuous support and updates are inevitable if you want to maintain the system's performance over time. Here’s what we usually do to make that happen:
- Regularly monitor the system to detect and address issues promptly, as it helps ensure consistent performance and reliability.
- Gather feedback from users to understand their experiences and identify areas for improvement.
- Periodically retrain the acoustic and language models with new data to adapt to changes in language usage, such as new slang, accents, or industry-specific terminology.
- Implement new features or improvements based on technological advancements and user needs.
- Stay updated with the latest security practices and compliance requirements to protect user data and adhere to regulations.
Speech recognition applications and use cases
There are numerous ways you can use speech recognition in real-world scenarios. Take a closer look at existing use cases in different industries.
Speech recognition solutions in healthcare
In the healthcare industry, AI speech-to-text systems have already changed how medical professionals interact with electronic health records. For instance, Nuance Communications' Dragon Medical One allows doctors to dictate patient notes directly into electronic health record systems (EHRs). Such an advancement reduces administrative workload and helps physicians spend more time with patients and less time on paperwork.
Speech recognition solutions in the automotive sector
In the automotive sector, manufacturers like BMW, Mercedes-Benz, and Tesla integrate speech recognition into their vehicles, which enables drivers to control navigation, climate settings, and entertainment systems without taking their hands off the wheel. For example, Tesla's voice commands allow drivers to set destinations, make calls, and adjust vehicle settings, enhancing safety and user experience.
Speech recognition systems for customer service
Customer service centers employ speech recognition to improve customer interactions. Companies like Bank of America use intelligent voice response systems that understand and respond to customer inquiries, streamlining support processes and reducing wait times. They have their own virtual assistant, Erica, who uses speech recognition to help customers with transactions, bill payments, and account information.
Speech recognition applications in the field of accessibility
Speech recognition technology empowers individuals with disabilities. Applications like Voice Access by Google allow users with motor impairments to control their devices entirely through voice commands. Similarly, Microsoft's Speech Recognition aids users in navigating their computers hands-free.
Speech-to-text models integrated into educational platforms
Educational platforms also try to make the most of speech recognition. Language learning apps like Duolingo use it to help users practice pronunciation and improve language skills by providing real-time feedback on spoken words. This interactive approach makes the learning experience much better and aids in language retention.
AI speech-to-text for transcription purposes
Moreover, transcription services such as Otter.ai and Rev utilize advanced speech recognition algorithms to convert speech into text. Thanks to this, their users can take notes during meetings, lectures, and interviews. Businesses use these services to enhance productivity and ensure accurate record-keeping.
Speech recognition solutions in retail
In the retail industry, companies like Walmart and Starbucks are experimenting with voice-activated shopping and ordering systems. Starbucks' voice ordering feature allows customers to place orders through their smart speakers or mobile apps using voice commands.
As you can see, there’s hardly an industry that doesn’t use speech recognition technology. Its ability to enhance accessibility, improve user experience, and optimize all sorts of operations makes it a critical component in the future of technology.
Why Opt For Uptech Services to Build a Speech Recognition System
Uptech is a reputable AI development company that has built over 25 innovative applications in the past three years alone. Our team has extensive experience in providing custom GenAI development and Machine Learning development services, and we're always excited to take on new challenges – speech recognition included.
Our background in AI projects means we're well-prepared to assist startups and small businesses in building speech recognition systems that can automate tasks, streamline processes, and enhance user experiences.
For example, we've worked on projects like:
- Hamlet – a tool that uses AI to make text summarization easy.
- Dyvo.ai – an AI app that helps businesses create eye-catching, brand-aligned product photos.
- Angler AI – a platform powered by AI to help brands significantly improve customer acquisition and lifetime value.
We developed Aboard AI to analyze real-time flight data using AI and Presidio Investor, a financial app that uses an AI agent to interact intelligently with extensive financial databases.
Our approach to AI development focuses on meeting the specific needs of your audience. From thorough product discovery to deploying scalable AI solutions, we make sure every project perfectly aligns with your goals.
If you're interested in AI consulting services for your speech recognition projects or have an idea you'd like to discuss, we're here to help. Feel free to contact us – we'd love to explore how we can bring your speech recognition ideas to life.
FAQs
What is an example of speech recognition?
An example of speech recognition is virtual assistants like Siri, Alexa, and Google Assistant, which can understand and respond to spoken commands to perform tasks like setting reminders, answering questions, or controlling smart home devices.
How can I improve my speech recognition system?
You can improve your speech recognition system by:
- Use high-quality microphones and reduce background noise.
- Implement algorithms to filter out ambient sounds.
- Train your models with more diverse and high-quality data.
- Fine-tune your acoustic and language models for better accuracy.
- Continuously update the system to adapt to new speech patterns and accents.
Can I create a speech recognition system like Siri or Alexa?
Yes, you can create a speech recognition system similar to Siri or Alexa, but it requires substantial resources, expertise in AI implementation and machine learning, and access to large datasets. Partnering with experienced AI developers, like Uptech, can help bring such a complex project to fruition.