Automatic Transcription Technology: How It Converts Audio Into Text For Transcription

Speech-to-text is an automatic transcription technology powered by Artificial Intelligence. It converts audio into text for transcription purposes in applications.

What is Speech-to-Text?

Speech-to-text is a technology that converts spoken audio into written text. It uses a combination of linguistics, computer science and electrical engineering to produce the text output. This text can then be consumed, displayed and acted upon by applications, tools and devices. This technology can help people who struggle with the physical act of writing. It can also reduce writing fatigue and allow students to focus more on content and organization. A microphone and software that recognize your voice might be beneficial when using a speech-to-text tool. Most smartphones now have built-in speech recognition software. It allows you to dictate messages or email and even control your device by voice.

A good Speech to Text application can understand multiple languages, accents and emotions. It can also distinguish between different speakers in mono-channel recordings. This feature, called speaker separation or diarization, can be found in speech-to-text applications as a real-time or batch API. A good Spanish speech to text service will have high accuracy levels, which is the most important aspect to consider when choosing a Speech Text provider. It is especially true for businesses that need to transcribe live audio or large recording files. Partnering with a speech-to-text company that utilizes human editors and automated technology ensures the highest level of accuracy.

Accuracy of Speech-to-Text

Speech-to-Text software analyzes vibrations and frequencies created by an individual’s voice to understand the nuances of spoken language. It breaks down the audio into small linguistic units known as phonemes, which are matched with words and phrases using a language model to create text outputs. This text can then be consumed, displayed and acted upon by applications, tools and devices as command input. Different speech-to-text software produces results at varying speeds and accuracy levels. A speech system’s accuracy is measured by its Word Error Rate (WER), the percentage of incorrectly transcribed words divided by the total number of transcribed words. WER is often compared with the error rate of human transcription to estimate how accurate a speech-to-text solution is.

When evaluating Speech-to-Text solutions, customers should consider the specific use cases and the environment they are deploying to ensure optimal performance in their scenarios. It includes testing the speech-to-text software with a test voice dataset that reflects real user variations over a set period. For example, some users may speak faster than others, and some users have very distinctive accents. A mismatch between the expected language in audio input and the spoken dialect can cause the speech-to-text software to produce incorrect text.

Latency of Speech-to-Text

Speech-to-Text is a software application, often powered by AI, that translates spoken words into word-for-word written text. You’ve probably used Speech-to-Text without realizing it — from Siri to videos with captions, speech recognition is everywhere. The speech-to-text process uses a microphone input and a cloud-based dictation processing system. The audio data is broken down into small linguistic units called phonemes, which are then run through complex algorithms to recognize and translate into text. The resulting text output can be typed into applications and features that support digital accessibility, such as voice control for devices like smart assistants or chatbots. It can be used to create transcripts or other written content.

For students with difficulty typing due to motor skill limitations, physical disabilities or blindness/low vision, the ability to compose via dictation can be life-changing. Students can create to-do lists, take notes during a lecture or recap the highlights of a meeting while on the go with comparable or better accuracy than humans. Speech-to-Text is also an essential part of the accessibility ecosystem, allowing businesses to provide live video captions for meetings or events and make podcasts and other audio content accessible for those with hearing impairments. It will enable employees and customers to participate in the workplace remotely and is a key component of the digital accessibility commitments made by many organizations.

Cost of Speech-to-Text

When using Speech-to-Text, users speak into a microphone, which converts audio input into written text. It’s common in smartphones and other mobile devices, where you can compose a message or enter commands by speaking into the device. The speech recognition software combines acoustic and language models to recognize what is being said. These models combine probabilities of word combinations to determine what is being said in the given audio input. It then transcribes the audio into Unicode characters that external applications, tools, and devices can consume, display, and act upon.

Speech-to-text software is becoming increasingly commonplace in the workplace. It enables fast, hands-free note-taking and provides a more efficient and accurate typing method. In addition, it increases workplace accessibility by allowing employees with disabilities to work more easily and effectively. This technology is also used to produce transcripts and captions for podcasts and videos to make content more accessible to individuals with hearing loss. While accuracy is undoubtedly the most important metric to consider when choosing a Speech-to-Text API, latency is another crucial factor. The longer the delay between audio input and transcribed text, the more frustrating it can be. To minimize the delay, you can train your speech engine to understand accents and inflections better and reduce interference by ensuring a good-quality microphone and removing sudden interruptions.


We monitors and writes about new technologies in areas such as technology, innovation, digitization, space, Earth, IT and AI.

Related Posts