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.
| Decade | Evolution of ASR |
|---|---|
| 1950s | Speech Recognition technology was first introduced by Bell Laboratories in the 1950s. The 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.’ Shoebox could understand and differentiate between sixteen spoken English words. |
| 1970s | Carnegie Mellon University in the year 1976 developed a ‘Harpy’ system that could recognize over 1000 words. |
| 1990s | After a long wait of almost 40 years, Bell Technologies again breakthrough the industry with its dial-in interactive voice recognition systems that could dictate human speech. |
| 2000s | This was a transformative period for ASR technology as the big technology giant Google started working on speech recognition technology. They created advanced speech software with an accuracy rate of approximately 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.
Automatic Speech Recognition (ASR) is a complex technology that relies on several key processes to convert spoken language into text. At a high level, the main steps involved are:
- Audio Capture: A microphone captures the user’s speech and converts the acoustic waves into an electrical signal.
- Audio Pre-processing: The electrical signal is then digitized and undergoes various pre-processing steps, such as noise reduction, to enhance the quality of the audio input.
- Feature Extraction: The digital audio is analyzed to extract acoustic features, such as pitch, energy, and spectral coefficients, that are characteristic of different speech sounds.
- Acoustic Modeling: The extracted features are compared against pre-trained acoustic models, which map the audio features to individual speech sounds or phonemes.
- Language Modeling: The recognized phonemes are then assembled into words & phrases using statistical language models that predict the most likely word sequences based on context.
- Decoding: The final step involves decoding the most probable word sequence that matches the input audio, taking into account both the acoustic and language models.
These core components work together seamlessly to enable highly accurate speech-to-text conversion, even in the presence of background noise, accents, and diverse vocabularies.
[ Also Read: What is Speech-to-Text Technology and How it works]
