Quick Concepts: Automatic Speech Recognition

What is Automatic Speech Recognition?

Automatic speech recognition, commonly known as ASR, is a technology that converts human voice into text using machine learning (ML) and artificial intelligence (AI). Over the past decade, the field has progressed dramatically with ASR systems appearing in apps that we use on a daily basis, such as TikTok and Instagram for real-time captioning, Spotify for podcast transcriptions, Zoom for meeting transcriptions, and much more. 

How Does ASR Work?

  1. The computer transforms the speech format into a spectrogram, a machine-readable representation of the audio file of the spoken words. 
  2. The acoustic model normalizes the volume and removes any background noises. Then the algorithm deconstructs the cleaned-up (wave file) audio representation into written words.  
  3. In order to determine full words, the automated speech recognition program analyzes phonemes in sequences using statistical likelihood. The NLP model is used to analyze the sentences from the sequences in order to comprehend the audio’s content, construct a suitable response, and respond using text-to-speech (TTS). 

ASR works in tandem with another AI-based language technology called natural language processing (NLP).  NLP can instruct an ASR engine on where to focus, while an ASR engine can help NLP better understand the context of words. 

Where is ASR Used?

Different speech technology applications are being used by a wide range of industries nowadays,  allowing businesses and consumers to save time and even lives. Popular use cases include: 

  • Automotive: Voice-activated navigation systems and search capabilities in car radios are made possible by speech recognizers, which increase driving safety. 
  • Tech: Virtual assistants are becoming more and more ingrained in our daily lives, especially on mobile devices. For tasks like voice search, we utilize voice commands to access them through our smartphones, such as Google Assistant or Apple’s Siri, or through our speakers, such as Amazon’s Alexa or Microsoft’s Cortana. They will only continue to be incorporated into the items we use on a daily basis, supporting the “Internet of Things” movement. 
  • Healthcare: To record and register patient diagnoses and treatment notes, doctors and nurses use dictation applications. 
  • Sales: There are a few ways speech recognition technology can be used in sales. It can assist a call center in transcribing thousands of customers and agent phone calls to identify frequent call patterns and problems. AI chatbots can converse with users via websites as well, responding to common questions and taking care of simple requests without the need to wait for a contact center representative to become available. In both cases, speech recognition technology speed up the process of solving customer problems. 
  • Security: As technology becomes more prevalent in our daily lives, security measures are becoming increasingly important. An acceptable level of security is added via voice-based authentication.  

Future of ASR

It is clear that ASR will have a significant impact on our lives in the future. To get machines to listen to us is a big deal, despite all the complexities, challenges, and technicalities involved. The main objective of ASR technology is to make applications more receptive to our speech. It may seem simple, but when given some thought, we understand how crucial this capability is. 

Getting Started with ASR

Scale your virtual assistants, ASR or text-to-speech models, conversational AI, wearables, and other NLP initiatives with Innodata’s end-to-end services. 

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