Automatic Speech Recognition (ASR) Software | Converting Audio to Text

Automatic Speech Recognition (ASR) Software | Converting Audio to Text

ASR speech technology has become part of everyday life for millions of people. We have gotten used to talking to Siri, Alexa or Cortana, which understand human voices in different languages. This short guide outlines the basic principles of ASR speech-to-text conversion, defines successful cases of implementing the technology and discusses its future.

What is ASR Technology?

Before we go in-depth on the mechanics of ASR, let's answer the question: What is automatic speech recognition?

ASR, or automatic speech recognition, is a technology that converts human speech into text. It also can be called a speech-to-text or transcription system. Automated speech recognition is most often used for Siri or Alexa assistance. It is also used in smart home tech, in-car command systems, and more.

Since the first speech recognition technology was introduced in 1950, it's come a long way. The most significant step in ASR was made in the 2000s with machine learning. Computers started to recognize the accent, pronunciation and context of human speech. In 2011, Apple introduced Siri, and a few years after, Microsoft launched Cortana, its own ASR speech recognition application. It was a big breakthrough to the advanced technology we use today.

How Automated Speech Recognition Works: The Basic Principles

The fundamental question of people interested in ASR is: How does automatic speech recognition work? The data for ASR speech technology consists of audio waves and text transcripts. The goal of the system is to define the context of audio before converting it into the appropriate text interpretation.

There are a few stages of the ASR speech recognition technique:

  • Voice Detection
  • Voice Segmentation
  • Decoding
  • Completing Transcription

Let us look at how each stage of automatic speech recognition works.

Voice Detection

First, we need to have audio data to construct deep learning models. It is essential to get a clear voice without noise. You can do it with Python libraries, for example. In practice, this process looks quite simple. You speak to your voice assistant, the speech converts to a wave file, the system cleans this file by removing background noise and stabilizing volume.

Voice Segmentation

The next step in ASR speech recognition is to define different speakers in the voice file. The system will create segments in the file and detect accents. The machine will also determine who spoke and when - marking each data segment accordingly. After that, ASR software can start decoding the audio file. The goal is to have the speech of each person identified.

Decoding

Voice recognition is the most volumetric part. At this stage, we don't have final sentences - only possible variants of phonemes for each part of the segment. There are three components of voice decoding:

  1. Lexicon. An essential step in speech decoding, it is a vital component of speech recognition, as many words can be pronounced differently. ASR software defines the most accurate recognition based on vocabulary at this stage. Lexicons must be customized for each language.
  2. Acoustic Model. Every person can pronounce the exact words and sounds in different ways. This decoding component aims to define signals into small-time parts and analyze the variety of each sound in context. This model is built on deep learning algorithms.
  3. Language Model. The other component of ASR speech decoding uses natural language processing (NLP) to ensure that computers understand the context of the audio. This model works the same way as the acoustic model by trying to predict the word order with a deep neural network.

Completing Transcription

The rescoring process is applied to each decoded segment of speech. As a result, you get a complete text with sentences in the proper context. There is a final stage when you get a result from ASR software in the form of a transcribed document.

Basic Types of ASR Software

There are two basic ASR software variants based on a speech type:

  1. Detecting a direct dialogue speech. A simple model of ASR. In this case, you can see a machine interface and a request to respond with a specific phrase or word from the list. For example, it is how automated customer service works by phone.
  2. Analyze Natural Language Conversation. It is a more complicated task for ASR software solutions, as it must recognize actual speech in different circumstances. The most common use of such software is Siri and Alexa.

Examples of ASR Application For Advanced Technologies

Applying ASR technology is one of the modern trends. We implement ASR speech recognition software primarily for voice assistants, but let us look at how this technology can be used.

  • Voice assistance is probably the most common use of ASR technology. Two-thirds (63%) of Americans use voice assistants, and the number is growing. These devices help people search for something on the web, send emails, call someone, change the temperature in the house and much more.
  • Transcription is another popular application of speech recognition solutions. It involves transcribing audio to text. It is widely used by podcasters and Youtubers to support audio and video by text and make voice notes by healthcare system workers.
  • Customer support in a call center has become more efficient by using software for communication with clients, especially if there are a lot of common questions to ask the client before connecting them to a natural person. It saves a lot of time and money for companies.
  • Language learning software. ASR helps students become more proficient in any language by analyzing their pronunciation and detecting mistakes. Many language learning apps, such as Duolingo or Babbel, use this technology to improve learners' accents.

Final Thoughts

The development of advanced ASR software takes a lot of time. But if we look at how this technology has changed since it was first introduced we see exciting results. And ASR speech recognition software will continue improving. The future of machine learning and deep learning is related to using ASR tools in the coming years. If you want to learn more or plan to apply an ASR solution to your business, contact our specialists: Cprime Studios can build such a solution at any stage of development.