Automatic Speech Recognition technology has been there for a long haul but recently gained prominence after its use became prevalent in various smartphone applications like Siri and Alexa. These AI-based smartphone applications have illustrated the power of ASR in simplifying everyday tasks for all of us.
Additionally, as different industry verticals further move toward automation, the underlying need for ASR is subjected to surge. Hence, let us understand this terrific speech recognition technology in-depth and why it is considered one of the most crucial technologies for the future.
A Brief History of ASR Technology
Before proceeding ahead and exploring the potential of Automatic Speech Recognition, let us first take a look at its evolution.
1950s
In the 1950s, Bell Labs created a virtual speech recognizer known as ‘Audrey’ that could identify the numbers between 1–9 when spoken by a single voice.
1960s
In 1952, IBM launched its first voice recognition system, ‘Shoebox,’ which could understand and differentiate between sixteen English words.
1970s
Carnegie Mellon University in the year 1976 developed a ‘Harpy’ system that could recognize over 1000 words.
1990s
After 40 years, Bell Technologies again breakthrough the industry with its dial-in IVR systems that could dictate human speech.
2000s
Google created advanced speech software with an accuracy rate of 80%, making it popular worldwide.
2010s
The last decade became a golden period for ASR, with Amazon and Apple launching their first-ever AI-based speech software, Alexa and Siri.
Moving ahead of 2010, ASR is tremendously evolving and becoming more and more prevalent and accurate. Today, Amazon, Google, and Apple are the most prominent leaders in ASR technology.
[ Also Read: The Complete Guide to Conversational AI ]
How Does Voice Recognition Work?
Automatic Speech Recognition is a fairly advanced technology that is extremely hard to design and develop. There are thousands of languages worldwide with various dialects and accents, so it is hard to develop software that can understand it all.
ASR uses concepts of natural language processing and machine learning for its development. By incorporating numerous language-learning mechanisms in the software, developers ensure the precision and efficiency of speech recognition software.
Here are some of the basic steps used in developing Automatic Speech Recognition software:
- Transmission of Voice into Electrical Signal: The vibrations of a person’s voice are captured using a microphone and transmitted into a wavelike electrical signal.
- Transforming Electrical into Digital Signal: The electric signal is further converted into a digital signal using physical devices like a sound card.
- Registering Phonemes to the Software: The speech recognition software then examines the digital signal and registers phonemes to differentiate between the captured words.
- Reconstructing Phonemes to Words: After processing the digital signal completely and registering all the phonemes, words are reconstructed, and sentences are formed.
To achieve the intended accuracy, the software leverages the trigram analysis method, which relies on using three frequently used words through a specific database. The ASR software is an exceptional technology that breaks down any audio pattern, analyzes the sounds, and transcribes those collected sounds into meaningful text and words.
[ Also Read: The Past, Present, & Future of Automatic Speech Recognition / Speech-to-Text ]
Real-World Examples of ASR
Automatic Speech Recognition is a terrific technology that has become widely popular and valuable today. Its high prominence is because it enables users to complete multiple tasks swiftly using hands-free control. The most popular products that use speech recognition technology are:
- Google Assistant
Developed in 2016, Google Assistant is the finest chat-based software today, having the highest accuracy rate of over 95% in US English. Roughly, it is used by hundreds of millions of people worldwide. - Apple Siri
Siri is the classic example of ASR’s availability in over 30 countries and 21 languages globally. Siri is the first chat-based system to revolutionize the use of speech-to-text technology. - Amazon Alexa
Alexa has become a household name and device today, with an estimated user count of over 100 million people worldwide.
Exploring More Use Cases for Speech Recognition Technology
Apart from using the ASR technology in chat-based software, there are other use cases of this exceptional technology. Here are a few of them:
Vehicle Speech Recognition
Today, we have the luxury of telling our car whom to call, which song to play, and where to set the destination. This all has become possible because of speech-to-text technology. This is a tremendous step in the safety aspect of your driving experience. By eliminating the need to interact physically with the screen, the use of ASR prevents loss of attention that may lead to an accident.
Transcription Services
ASR technology has streamlined the transcription process, enabling rapid and accurate conversion of spoken content into written text. This has proven invaluable for industries such as journalism, legal, and medical sectors, where accurate and timely transcriptions are crucial.
Call Centers & Customer Support
Call centers have embraced ASR systems to transcribe customer interactions, allowing for better tracking, analysis, and quality control. By converting spoken conversations into text, ASR enables call center agents and managers to review customer interactions and extract valuable insights to improve their services.
Language Learning
ASR technology has revolutionized language learning by providing real-time feedback on pronunciation and spoken language skills. This enables learners to refine their speech patterns, receive immediate corrections, and improve their fluency in a more efficient manner.
Accessibility for the Hearing Impaired
ASR systems have been instrumental in breaking down communication barriers for individuals with hearing impairments. By converting spoken language into written text, ASR technology provides real-time captioning services, making audio content more accessible to a wider audience.
Voice Biometrics and Security
The unique characteristics of an individual’s voice can be utilized as a form of biometric authentication. ASR technology plays a crucial role in voice biometric systems, offering an additional layer of security for personal identification and access control.
What Does the Future Hold for ASR Technology?
With the advancement of AI and machine learning, Automatic Speech Recognition technology is expected to become more accurate, faster, and more natural-sounding. In addition, ASR technology is likely to become prevalent in customer service, education, healthcare, and more. For organizations, developing customized ASR-based business solutions must be the next target.
Originally published at https://www.shaip.com.
