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The basics of ASR

While we are still far from developing artificial intelligence systems that are truly autonomous, in many ways, we are gradually advancing toward the development of Automatic Speech Recognition technology at a rapid pace. In more ways than one, this technology is proving to us that it will be able to provide its users with valuable experiences. Eventually, ASR will play a significant role in developing various applications.

Hence, it now begs the question, what is ASR technology, how does it work, and what are its benefits? Thus, this article will delve into this swiftly advancing technology to provide an overview of ASR and its uses.

ASR is the technology revolutionizing the field of technology and, in layman’s terms, can be defined as the utilization of computer systems to convert spoken words into written ones. At the heart of a majority of the most advanced forms of some ASR systems in Natural Language Processing (NLP) which helps facilitate conversations between the users, i.e., humans and AI.

ASR deals with the recognition and translation of a myriad of spoken languages into text by a process called “speech to text.” However, the speech recognition process also has subfields such as voice recognition and speaker identification. Each specializes in the identification of both spoken content and speakers’ identity.

So, how does ASR work?

At present, there are two types of speech recognition systems available. These systems are called speaker-dependent and speaker-independent systems.

There are two types of speech recognition systems: speaker-dependent and speaker-independent. Generally speaking, speaker-dependent systems require enrollment training, which involves reading text or a series of discrete words into the system. The algorithm analyzes the recordings and links them with the text collection. A speaker-independent system does not rely on vocal training to recognize speech.

Furthermore, there are a few steps involved in the process of speech recognition, and they are:

  • Conversion of speech:Usually, speech is recorded and made available to a computer system in the analog format, which needs to be converted to digital format. This digitizing of the speech is done through sampling and quantization techniques or standard sampling techniques. Typically, a one-dimensional vector of the voice sample, an integer, is utilized to represent digital speech.
  • Speech Pre-processing:Background noises and long periods of silence are often recorded during conversations. Hence, it is necessary to identify and remove these frames from the entire speech. Signal processing techniques are utilized to reduce or eliminate the noise. The whole recording/speech is divided into 20-second extended frames used in the feature extraction stage.
  • Feature Extraction:During this stage, the speech is converted into frames which are converted into a feature vector that can specify which phoneme or syllable, also known as features, has been spoken in the speech.
  • Word Selection: Once the computer system has identified the phonemes/features, the sequence of generated content is translated into spoken language using language or probability models.

How is ASR used?

Many industries today have started utilizing automatic speech recognition for various functions. Let’s look at some of the uses of automatic speech recognition  below:

  • It is utilized in legal proceedings to ensure that every word uttered is captured without error. Additionally, ASR allows for digital transcription and provides users with the ability to scale.
  • In higher education institutions such as universities, this technology is used to provide learners with disabilities with live captions and transcriptions in the classrooms. It also benefits students who are not native speakers of the language and others with varying needs.
  • ASR is also utilized by the healthcare industry, primarily by doctors. It helps doctors transcribe notes from patient meetings or to document steps during the various surgeries performed daily.
  • Media production companies also utilize ASR to provide viewers with live captions and transcribed speech for their various productions.
  • Corporations use ASR to caption and transcribe speech to provide employees with accessible training materials to ensure that the overall environment is inclusive and cater to employees with differing needs.

What are the advantages of ASR?

There are many advantages to using ASR. They include:

  • It provides users with a high and accurate recognition rate.
  • Its flexible access mode can support SDK access to different devices.
  • It can provide users with a fast response and enhances the user experience.
  • ASR can be used to optimize a variety of jobs across various industries.
  • ASR can ensure intelligent text error collection, which analyzes industry text data and corrects any errors detected based on the user input context.

Conclusion

Despite its humble beginnings, speech recognition technology has become a worldwide phenomenon. ASR in Artificial Intelligence has many advantages, from big companies to independent people; everyone wants to make their mark in the speech recognition world, from doctors to lawyers to police officers to university professors.

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