27 APRIL 2021

What is Automatic Speech Recognition and How Does It Work?

5 min read
Automatic Speech Recognition or ASR is a technology that directly affects our everyday lives. Voice assistants like Siri or Alexa are built using ASR, and the scope for the application of this technology is continually growing. But how does it actually work? Today we will take a closer look at the fundamentals of the ASR, its use cases, and perspective.

Behind the curtains of ASR technology

The central concept of Automatic speech recognition is that we have a technology that can process human speech and convert it into text. This text can be used for multiple purposes, including the translation commands to AI-based assistants like Google Voice, Alexa, or virtual agents for call centers like the ones developed by Neuro.net.

So, the ASR is the first step in the process of the voice-powered machine-human interaction. To make the speech recognition efficient and reduce the error rate, the ASR system needs to be trained with the diverse data and speech samples. Such samples should cover multiple use cases, including accents.

The process of automatic speech recognition looks like this:
  • The person speaks, and the ASR solution detects speech.
  • The audio file that contains words that the machine understood created afterward.
  • As the file contains additional unnecessary data like background noise, it is then cleaned up.
  • At the next stage, the system breaks the speech into phonemes and sequences of these phonemes.
  • Finally, the software analyzes these sequences, tries to determine the word, and then combines multiple words in sentences.

Components of ASR

ASR translates audio into texts by creating a digital representation of spoken words. This is done by working with phonemes. These are small pieces of the audio, i.e., the smallest unit of sound that may affect the specific word's meaning in a particular language.

Phonemes do not have their own meanings, and for ASR, there are usually specific sets of phonemes used. ARPABET is an example of such a collection, and it contains 39 phonemes. The list may be longer and depends on the target language we'd like our ASR to be able to understand.

To make it all possible, the ASR consists of several components, including:

  • The decoder (also called recognizer or ASR) performs the recognition. This component receives an audio input and then generates the so-called recognition hypothesis.

  • The language model includes all phrases the ASR system can recognize and a unique dictionary with phonetic representations of specific words.

  • The acoustic model provides additional data like the language or dialect. For better efficiency, it is recommended to use a US English acoustic model to recognize the person's speech from the US.

With an ASR, it is possible to literally talk to computers like humans. Voice assistants like Siri, Alexa, or Google, are not the only use-cases. There are more and more cases when people talk to computers and even do not realize this!

ASR and its real life applications

The development of the ASR technology took decades. Nowadays, it has multiple use cases, including language learning, hands-free computing, or better accessibility for those hard of hearing. Still, the main application is enabling human-to-machine communications.

With an ASR, it is possible to literally talk to computers like humans. Voice assistants like Siri, Alexa, or Google, are not the only use-cases. There are more and more cases when people talk to computers and even do not realize this! For example, there are AI-fueled contact centers, where customers are connected to virtual agents trained on real conversations' data. Such virtual agents can answer with a human-like and almost indistinguishable voice. They understand everything people tell and offer solutions for their requests.

Such intelligent systems can help people find answers to almost every question that no human customer care representative can do. Also, only 1% of customers understand they are talking to an AI-based agent and not a human. Software that we at Neuro.net build can mimic a human experience, including emotions and even random yet well-placed interjections such as "umm" and "aha." All these factors make speech more human.

The ASR is one of the modules of such solutions, helping an AI understand the person and develop the best solution possible.
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Final thoughts

ASR is developing fast, adding more and more use cases. The world where computers can correctly understand natural language and generate high-quality replies opens up many new possibilities.

However, building an ASR that works for multiple languages and does not require a complex training and lengthy implementation process is challenging. The number of solutions that fit these requirements is not that broad. However, even now conversational AI technology offers a 98% recognition accuracy* with only several thousands of call recordings.

* F1 score for intent classification

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