Thanks to the ongoing development of Automatic Speech Recognition technology, we are rapidly approaching the potential future scenario.
Examining the history of computer science reveals distinct generational lines that are defined by the input technique. How does information travel from our brains to the computer? We can link computing gains to digital interfaces from punch-card computers through keyboards to pocket-sized touch displays. As is often the case with technology, our question is “what’s next?”
The answer is the human voice. ASR (Automatic Speech Recognition) is the technology that facilitates this change. Developers in various industries now use automatic speech recognition to improve corporate productivity, application efficiency, and digital accessibility. This article provides a comprehensive introduction to automatic speech recognition.
Automatic speech recognition meaning
Automatic speech recognition technology is capable of turning spoken words (an audio stream) into command-like written text.
The most modern software development of the present day can accurately process dialects and accents of several languages. Automatic speech recognition is prevalent in user-facing applications such as virtual agents, live captioning, and clinical note-taking. These use cases necessitate accurate speech transcription.
Speech AI developers also use terms such as speech-to-text (STT), and voice recognition to describe automatic speech recognition.
Automatic speech recognition is a crucial component of speech AI, which is meant to facilitate voice communication between humans and computers.
Insights into the speech recognition algorithms
Automatic speech recognition can be developed traditionally by using statistical algorithms. Another way is by using deep learning techniques such as neural networks to convert speech into text.
Traditional ASR algorithms
Hidden Markov models (HMM) and dynamic time warping (DTW) are examples of such traditional statistical voice recognition approaches.
An HMM is trained to predict word sequences from a set of transcribed audio samples by optimizing the model parameters. The objective is to maximize the likelihood of the observed audio sequence.
DTW is a dynamic programming approach that determines the optimal word sequence by calculating the distance between time series representing unknown speech and known words.
Deep learning ASR algorithms
In the last few years, developers have been interested in deep learning for speech recognition because statistical algorithms aren’t as accurate. Deep learning algorithms are better at understanding dialects, accents, context, and multiple languages. They also transcribe correctly even in noisy environments.
Quartznet, Citrinet, and Conformer are three of the most well-known acoustic models for speech recognition that are up-to-date. In a typical speech recognition pipeline, you can choose and switch any acoustic model you want based on your use case and performance.
Voice and automatic speech recognition technology is becoming the foundation for numerous advanced voice services.
Fortune Business Insights projects that the global Automatic Speech Recognition Market Size will reach USD 49.79 billion by 2029. It expanded at a CAGR of 23.7% during the forecast period (2023–2029).
What follows are a few of the current trends in this market.
Consumer electronic devices: A daily chores optimization
Automatic speech recognition is being incorporated into more consumer devices every day, including televisions, refrigerators, washing machines, fans, and lighting.
For example, Amazon Alexa is integrated into the new GE Profile Top Load 900 series washing machine. GE appliances utilize the Amazon voice assistant to play music, deliver jokes, etc.
Also, if you have a terrible stain on a shirt and need assistance removing it, you can look online for solutions. However, in this washing machine, Alexa will perform the task for you. The organization claims that it strives to provide customers with a personalized experience.
Voice-activated machines have the unique ability to respond to orders. For example, they can wash cotton clothing, remove pen ink, and wash whites by responding “optimizing the washer.” Customers are essentially offered hands-free control of washing machines.
Friendly smart cars: Cooperation for development
Automobiles and the technologies they incorporate have grown together over time. Most automobiles are equipped with an abundance of functions, but using them while driving can be distracting. Consequently, more businesses are considering implementing automatic speech recognition features.
As a part of its “Toyota Connected” technology, Toyota has recently created automatic speech recognition. The company introduced a new Intelligent Assistant system that responds to the driver’s commands.
The very sophisticated automatic speech recognition learns the orders and becomes more intelligent over time. If the driver desires coffee, for instance, the assistant will display a map containing all nearby coffee shops.
Speech recognition for children: The next frontier
Sensory, a leader in edge AI, has recently unveiled an automatic speech recognition algorithm designed specifically for children. It is specially designed to recognize a child’s voice and linguistic patterns.
This ASR technology applies to toys, child wearables, and educational technology. However, speech identification of children is a difficult task due to the paucity of accessible training data.
General plus Technology, a global provider of integrated circuits for toys and speech, has incorporated Sensory’s innovative voice recognition system for children. Customers have an increased desire for toys. In the market for automatic speech recognition, similar developments are anticipated to occur frequently.
Top speech recognition advantages in common fields
Finance — Revolutionizing voice for the financial sector
In the finance industry, automatic speech recognition is utilized for applications such as call center agent assistance and trade floor transcripts. ASR technology can transcribe interactions between clients and call center representatives or traders on the trading floor. The studied transcriptions can subsequently be used to give agents with real-time recommendations. This contributes to an 80% decrease in post-call time.
Moreover, the generated transcripts are utilized for subsequent tasks:
- Sentiment analysis
- Text summarization
- Question answering
- Intent and entity recognition
Telecommunications — The impact of voice in modern telecom sector
Contact centers are crucial to the telecommunications sector. With contact center technology, you can reimagine the telecommunications customer center, and automatic speech recognition facilitates this.
Automatic speech recognition is used in telecom contact centers to transcribe conversations between customers and contact center agents. The goal is to analyze them and recommend call center operators in real time.
Unified communications as a software (UCaaS) — Innovation expanded through pandemic
COVID-19 increased demand for UCaaS solutions. Accordingly, manufacturers began focusing on the usage of speech AI technologies like ASR to offer more engaging meeting experiences.
For instance, automatic speech recognition can be used to create live captions in video conferencing meetings. The generated captions can then be utilized for tasks such as writing meeting summaries and identifying action items in meeting notes.
ASR technology challenges: Is it worth the investment?
Continual progress toward human-level precision is currently one of automatic speech recognition’s greatest obstacles. Even though both ASR systems — classic hybrid and end-to-end Deep Learning — are substantially more precise than ever before, neither can boast human-level precision.
Because there are several nuances in the way we talk, including dialects, slang, and pitch. Without significant effort, even the finest Deep Learning models cannot be trained to encompass this extensive tail of edge cases.
Some believe that specialized Speech-to-Text models can solve this problem of accuracy. In practice, custom models are less accurate, harder to train, and more expensive than a decent end-to-end Deep Learning model. Unless you have a highly specialized use case, such as recognizing children’s speech, this is the case.
The privacy of automatic speech recognition technology is another major concern. Too many large automatic speech recognition firms utilize user data without specific consent to train models, generating grave issues about data privacy.
Continuous data storage in the cloud also creates security concerns, particularly if unprocessed audio or video files or transcribed text contain Personally Identifiable Information. Developers must come up with IT software development solutions to ensure the privacy of ASR technology.
Thanks to ongoing data collection and cloud-based processing, many large voice recognition systems no longer have trouble distinguishing accents.
They are now able to recognize a greater diversity of words, languages, and accents. This is accomplished through large-scale data collection programs and the assistance of language specialists from all over the globe.
Here is an example.
Sonos was building a connection between its wireless speakers and smart home assistants and sought speech data from three countries — the United States, the United Kingdom, and Germany — divided by age group.
They required specific wake word information, such as Amazon’s “Alexa” and Google’s “Hey Google.” This information would be used to test and fine-tune the wake word recognition engine, ensuring that customers of all demographics and accents enjoy a similarly superior voice experience on Sonos devices.
The project requires precise demographic and proportional sampling. Participants were monitored according to their accents and ranged in age from 6 to 65, with a 1:1 ratio of males to females.
This also featured participants of several ethnic backgrounds in the United States: Southeast Asian, Indian, Hispanic, and European.
Sonos was ultimately able to extend the voice recognition capabilities of their speakers to include new English and German dialects.
In addition to what we’ve already mentioned, these types of initiatives will open the way to a plethora of speech-controlled devices. These devices can be integrated with the voice technology of prominent digital assistants, such as:
- household appliances
- security devices and alarm systems
- personal assistants
Automatic speech recognition is a field in development. It is one of the various methods individuals can connect to computers without having to type extensively. Automatic speech recognition has one straightforward objective despite its many complexities, challenges, and technicalities: to make computers respond to us.
We take this quality in one another for granted, but when we stop to consider it, we realize how essential it is. As children, we learn by paying close attention to our parents and teachers. We develop our ideas by listening to the people we meet, and we maintain healthy relationships by listening to one another.