Whisper AI and ASR Technology

Oluwafemi Smith
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Feb 04, 2023

Wikipedia identifies speech recognition as an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers, with the main benefit of searchability.

In some other circles, speech recognition is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text (STT). Regardless, this process incorporates knowledge and research in the computer science, linguistics, and computer engineering fields.

Having built a line of thought on ASR, today’s post will highlight one of the inventions of ASR called Whisper software. At the end of this read, you will find out what Whisper does, how it works, its edge over other similar applications, and its shortcomings.

What is Whisper AI?

Whisper is an automated speech recognition (ASR) system that has been trained on 680,000 hours of supervised, multilingual, and multitasking online data. We demonstrate that using a dataset of this size and diversity increases the robustness of accents, background noise, and technical language.

Additionally, Whisper permits translations into English from several languages as well as text transcription in those languages. With their mode of sharing models and inference code to lay the groundwork, future research on robust speech processing as well as the creation of useful applications is easily attained.

What is Whisper’s architecture like?

The encoder-decoder transformer is how the Whisper architecture is implemented, which is a straightforward end-to-end strategy.

An encoder receives input audio that has been divided into 30-second segments and transformed into a log-Mel spectrogram. With the help of specific tokens that instruct the single model to carry out tasks like language identification, phrase-level timestamping, multilingual voice transcription, and English-to-speech translation, a decoder is trained to anticipate the matching text caption.

Where can we apply ASR in the modern day?

Automatic Speech Recognition (ASR) technology can be applied in a wide variety of modern-day applications. Some examples include:

Voice-controlled personal assistants: ASR is used to interpret voice commands and perform tasks on smartphones, smart speakers, and other devices.

Speech-to-text: ASR is used to transcribe spoken words into written text for applications such as dictation software and captioning for videos.
Voice biometrics: ASR is used to authenticate users based on their unique voiceprint.
Call centers: ASR can be used to automate customer service tasks and improve call routing.
Automotive: ASR can be used in cars to provide hands-free control of the car's functions such as navigation and media playback.
Accessibility: ASR can be used to help individuals with disabilities, such as those who are visually or physically impaired, to interact with technology.
Language Translation: ASR can be used in combination with machine translation to provide real-time spoken language translation.

What are the negative effects of using ASR?

There are several potential negative effects of using Automatic Speech Recognition (ASR) technology, including:

Privacy concerns: The use of ASR technology can raise privacy concerns, as it requires the collection and storage of large amounts of spoken data.
Error rate: ASR systems are not perfect, and they can make mistakes when interpreting speech, leading to inaccuracies or misunderstandings.
Bias: ASR systems can be trained on biased data, which can lead to errors that disproportionately affect certain groups of people.
Limited recognition capabilities: Some ASR systems may not be able to recognize certain accents, dialects, or languages, which can lead to inaccuracies or the exclusion of certain groups of people.
Lack of understanding of context: ASR systems may not be able to understand the context of Job displacement: Some industries such as customer service may see job displacement as the technology improves, which can harm the workforce
Dependence on technology: Overreliance on ASR technology can lead to a decrease in people's ability to communicate and understand speech effectively, which can be a problem, especially for children and the elderly.

Conclusion.

Whisper remains one of the breakthroughs of ASR technology, which allows computers to interpret and understand spoken language. With Whisper, you can transcribe speech into written text and interpret voice commands.

It can be used in a wide range of applications, such as voice-controlled personal assistants, speech-to-text, voice biometrics, call centers, automotive, accessibility, and language translation. However, ASR technology can make mistakes, raise privacy concerns, be affected by bias, have limited recognition capabilities, lack understanding of context, lead to job displacement, and decrease people's ability to communicate effectively.

It's important to note that researchers and engineers are working to improve these systems and minimize the negative effects. Furthermore, the benefits of ASR may outweigh the negatives depending on the application and context.

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