Defining Automatic Speech Recognition and Exploring How it Works
With technology all around us, it was only a matter of time before we could speak to computers and have them understand. Automatic speech recognition, or ASR, enables human communication with computer systems that result in text, captured data, and/or a spoken output.
We’re going to define the automatic speech recognition meaning, and see how AI automation speech recognition has a myriad of applications across business and personal settings.
What is ASR?
Automatic speech recognition is technology that enables humans to speak to computers in the same way that humans conversate with one another.
What once began as a system that could understand just a few sounds has evolved into sophisticated technology that can learn industry-specific jargon, even on the spot, without having any prior training.
With the proliferation of ASR over the last decade, it seems like it’s a new innovation. However, automatic speech recognition can be traced back to as early as 1952, with the introduction of Bell Labs’ Audrey, which initially understood spoken digits. Later, researchers made it possible for Audrey to transcribe spoken and simple words.
Today, ASR technologies are ubiquitous and powerful. From social media functions that transcribe spoken words to speech-enabled hands-free tools like aiOla that know business-specific jargon, across industries, in any language, accent, and acoustic environment, ASR solutions are transforming how we work and play.
How ASR Works
Speaking to computers and actually having them understand feels like something out of a sci-fi movie, but it is now the new norm.
But, how exactly does ASR work?
Automatic speech recognition combines artificial intelligence (AI) and machine learning (ML), through the use of either deep learning models or a traditional hybrid approach.
A traditional hybrid approach brings together Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM), requiring forced aligned data. Forced aligned data means that the system must determine where in time particular words have been spoken using a text transcription of audio speech.
To make this possible, the process run through critical models, namely:
- Lexicon Model: A description of how words are phonetically pronounced.
- Acoustic Model: The acoustic model outlines acoustic patterns of speech to predict where each sound is spoken during each speech segment.
- Language Model: The language model is focused on statistics of language, which is used to determine what word is most likely to be spoken next based on the prior word.
- Decoding: Decoding combines the above models to produce a transcript.
The downsides of the traditional hybrid approach are that:
- It doesn’t produce the most accurate outcome
- It requires independent training for each model
- It is time-consuming and intensive
Alternatively, another approach is called end-to-end deep learning, which doesn’t require forced alignment.
Instead, end-to-end deep learning can be trained to map a sequence of acoustic features into a sequence of words. It’s a less labor intensive approach, with greater accuracy when compared to the traditional hybrid approach.
Types of ASR Systems
There are a few different types of automatic speech recognition systems on the market. These span:
Speaker-Independent Systems
Any system that can recognize speech from anyone, no matter their characteristics.
Speaker-Dependent Systems
Speaker-dependent systems are trained for a specific person or group of people to use (i.e. biometrics).
Continuous Speech Recognition
A system that can understand speech from unbroken sentences.
Isolated Word Recognition
On the other hand, isolated word recognition systems can recognize short phrases or individual words.
Key ASR Technologies
Automatic speech recognition models encompass several different technologies, each one contributing to enhancing the accuracy and efficiency of these platforms. Here, we’ll explore the fundamental technologies that define ASR to better understand the systems used to extract written words from speech.
Hidden Markov Models (HMMs)
HMMs are a statistical model that relies on machine learning (ML) to uncover sequencing in an input. When used with audio, HMMs work to observe the relationship between an acoustic signal and underlying linguistic units, which helps approximate the outcome of the speech.
Deep Learning and Neural Networks
Deep neural networks, specifically recurrent and convolutional architectures, allow ASR platforms to learn the intricacies and patterns of speech data. This helps the text become more accurate and robust.
Natural Language Processing (NLP)
NLP is essential to ASR outputs, as it helps integrate the context of words and language with understanding, further enhancing the accuracy levels of transcriptions. With NLP, ASR systems can capture not only the words said aloud in speech, but also the intent and meaning behind them.
Speaker Diarization
With speaker diarization technology, the focus is on distinguishing between multiple speakers in a conversation. This is essential for applications that need speaker-specific data, such as meeting transcription or AI assistants that are voice-controlled.
End-to-end ASR Systems
An end-to-end ASR system combines different stages of a traditional ASR pipeline into a single integrated model. These systems have become more popular due to increased computing power and vast quantities of training data, and rely on technologies like HMMs and deep learning to offer better performance while relying on less manual engineering.
Challenges in ASR
Despite the many strides that ASR technology has made over the years, it still isn’t entirely perfect (is anything really?). Some of its common challenges are:
- Understanding Different Dialects and Accents
- Handling homophones and contextually similar phrases
- Background noise and cross-talk
- Real-time processing capabilities
- Data privacy and security concerns
As a first-of-its-kind proprietary technology, aiOla has combined automatic speech recognition (ASR) and natural language understanding (NLU) through a unique training module. As a result, aiOla knows jargon that is specific to your business (without having to be trained first), can understand any language and accent. Importantly, aiOla works with accuracy in any acoustic environment, making it a gamechanger for workers in noisy industries such as manufacturing, logistics, aviation, and more.
ASR Applications: How is it Used?
The voice recognition market reached nearly $12 billion in 2022 and is expected to grow to nearly $50 million by 2029, highlighting the massive increase in demand for ASR-driven technologies.
Today, ASR is used in many different use cases and applications, ranging from high-level business and data collection to everyday technologies like virtual assistants. ASR is versatile enough to appeal to all types of users of any technological background, which is why it’s truly reshaping the digital landscape. Let’s look at some of its most common applications.
Voice Command Systems
From smart home systems to in-car navigators, ASR plays a pivotal role in voice command systems. These systems are activated by voice and enable users to interact with a device through spoken commands alone. Users can do things like initiate phone calls, navigate to an address, or change the temperature using only their voice, with ASR turning words into actions.
Transcription Services
ASR has changed the way transcription platforms and services operate. Transcription platforms are used in several fields, such as journalism, legal offices, medical care, business, and in education as well. With ASR technology, language can be transcribed into text a lot quicker, easier, and with higher accuracy, turning manual note-taking into a much more efficient affair.
Voice Search and Virtual Assistants
Voice assistants like Siri or Alexa use ASR to turn commands into reminders, answers to voiced questions, or completed tasks on a mobile device. 50% of US consumers use voice search on a daily basis. ASR technology makes this possible by enabling virtual assistants to search the internet or trigger an action through speech commands alone.
Accessibility Tools for Differently-abled
When humans can interact with technology through voice alone and completely hands-off, these systems become more accessible to those with impairments. ASR is what allows differently-abled people to engage with technology, fostering a sense of independence and inclusivity in multiple digital interactions.
Customer Service and Call Centers
ASR can filter and automate call handling in a call center, making customer service more intuitive and efficient. Calls can be better routed to the right agents based on data gathered from the caller through speech, which works to both improve the level of service and optimize workflows. An analysis by McKinsey shows that ASR tools can drastically improve customer service in call centers by reducing handle time, improving self-serve options, and creating better interactions.
Language Translation
Aside from just transcribing speech to text, when paired with translation software, ASR is instrumental in converting one language to another. This can facilitate cross-cultural communication and break down language barriers in industries like tourism and even business settings.
What’s the Future of ASR?
When innovation is made upon technology, it happens exponentially. This means that the next few years of ASR’s growth will feel like decade’s worth, or even more, of progress.
Some trends that are expected include:
Speech-to-Speech Translation
Companies are working on creating ASR that can translate spoken words between languages in real-time.
Multi-Modal Inputs
Rather than just being able to decipher spoken words, ASR tools are learning how to assess body language, facial expressions, and gestures in an effort to increase accuracy.
Edge-based Processing
Rather than having to use cloud computing to process input to output, there’s a focus on being able to run automatic speech recognition algorithms on the device itself.
The Bottom Line
Automatic speech recognition has opened the door to endless possibilities in the realm of personal and professional life. In business settings, ASR tools, like aiOla, make it possible to complete critical tasks, hands-free, and with utmost accuracy using nothing more than words. In home settings, ASR solutions are like having a personal assistant. In many cases, they’ve been able to increase accessibility for people worldwide.
