Explaining the basics of automatic transcription, its advantages and disadvantages, and how to use it!

The basics of automatic transcription and how to use it

What is automatic transcription?

Automatic transcription is a technology that converts audio data into text. It uses AI and speech recognition technology to automatically convert human speech into text in real time or from pre-recorded audio data.

Automatic transcription is often used in situations such as meetings, lectures, minutes, and interviews, and helps to improve the efficiency of recording work and the searchability of text information. Recently, it has become easy to use through smartphone settings and PC apps, and is used widely in both everyday life and business.

Utilizing automatic transcription

Automatic transcription makes it possible to use information in a variety of everyday situations.

■ Records of classes and lectures

By converting audio into text, you can eliminate the need to take notes and review the information in the text.

■ Creating meeting minutes

Spoken audio is automatically converted into text, allowing you to quickly create and share documents and minutes.

■ Adding subtitles to video and audio content

It is now possible to provide easy-to-understand information to viewers on YouTube, podcasts, etc.

■ Call records

Audio data from customer interactions and sales activities can be converted into text and used for complaint handling and improvement activities.

■ Preparation for multilingual translation

Improved translation accuracy by automatically converting foreign language audio information into text

How automatic transcription works

Voice Recognition Technology

Automatic transcription is a system that uses speech recognition technology to convert spoken words into text, and is one of the technologies made possible by the application of speech recognition technology.

To understand how automatic transcription works, it's important to understand the basics of speech recognition technology. Speech recognition, a technology in which a computer analyzes and converts spoken speech into text, primarily combines acoustic signal processing and natural language processing. Audio input from a microphone or other device is first converted into a digital signal, followed by preprocessing such as noise reduction and speech segmentation. Features such as pitch and intensity are then extracted using techniques such as MFCC (Mel Frequency Cepstrum Coefficients). These features are then analyzed by a deep learning-based acoustic model and recognized as phonemes (the smallest units of language). A language model then converts the phonemes into words or sentences based on the context, resulting in the final output text. In recent years, the introduction of Transformer-type models and attention mechanisms has enabled more complex contextual understanding and text conversion that reflects the intent of the utterance. This has led to its widespread use in a wide range of applications, including smart speakers, meeting minutes, medical records, and translation assistance. Speech recognition continues to evolve daily, moving beyond simple listening to realize meaningful understanding and dialogue.

Machine Learning

Machine learning is not simply a program; it is a self-learning, self-improving system. Machine learning in automatic transcription is a core technology for understanding not only the sounds but also the meanings of speech and converting them into text. First, audio is collected using a microphone or other device. After preprocessing, such as noise reduction and volume adjustment, frequency and temporal changes are quantified as features. At this stage, techniques such as MFCC (Mel-Frequency Cepstrum Coefficients) are used to extract the components of the sound. Next, a deep learning acoustic model analyzes these features and recognizes phonemes, the smallest units of language. Advanced language models such as Transformer then convert phonemes into words and sentences based on context and lexical patterns, outputting natural-sounding sentences. In this way, machine learning not only understands the structure of speech but also accurately converts spoken language into text, taking into account linguistic meaning.

Pros and cons of automated transcription

Here are some benefits of automated transcription:

Increased efficiency of information processing

It's significantly faster than manual transcription, with results available in just a few minutes. This saves you time and allows you to focus on other important tasks. Since the contents of meetings and interviews can be instantly converted into text, the time it takes to take minutes and summarize the results is significantly reduced. Real-time transcription also instantly displays what's being said as text, eliminating the question, "What did they just say?" and enabling smoother communication. Furthermore, it provides peace of mind knowing that information can be supplemented with text even in environments where it's difficult to hear the audio.

Smooth information organization and utilization

Converting audio to text centralizes information, facilitating classification and tagging, while allowing it to be saved and shared in a highly searchable format. This reduces unnecessary reconfirmation and facilitates documentation and knowledge accumulation. By converting audio from meetings, interviews, lectures, and other events into text in real time or from recordings, this tool allows for centralized and systematic organization of information. Converting audio to text makes content search and review easier, eliminating the need to repeatedly listen to recordings. Furthermore, storing audio as text allows for classification and tagging, allowing for efficient extraction of information related to specific topics or speakers later. Furthermore, this organized data can be directly used to create documents such as minutes and reports, significantly improving the speed and accuracy of work. In the long term, accumulating text data as knowledge allows for continuous utilization as an information asset within an organization, going beyond mere recording and supporting strategic information utilization. Information that was previously "heard and then forgotten" can be converted into a "usable" format for storage and utilization.

Utilizing the latest technology

Advances in AI speech recognition technology have enabled automatic transcription to convert speech into text with greater accuracy and speed, enabling innovative information organization in a variety of fields, including business, academia, medicine, and media. By utilizing cloud-based services and real-time transcription functions, conversations and meetings with multiple people can be recorded and shared simultaneously, regardless of location. Text data can then be directly used for AI summarization, sentiment analysis, translation, keyword extraction, and more, transforming the value of information from simple recording to knowledge utilization. Furthermore, speech recognition models using machine learning adapt to the speaker's voice quality and speaking style, enabling accurate recording of complex conversations and technical terminology that were previously difficult to capture, significantly improving the accuracy and speed of information organization. By utilizing these cutting-edge technologies, automatic transcription has gone beyond being a convenient feature and has become a core tool for boosting intellectual productivity and work efficiency.

Specifically, these are technologies that many people have heard of, such as Whisper and Gemini. These technologies are based on AI speech recognition, natural language processing (NLP), deep learning, etc., and are selected based on the application, accuracy, processing speed, and security requirements.

Disadvantages of automatic transcription

While automated transcription is convenient, it's not perfect. There are several points to keep in mind. First, speech recognition accuracy has limitations, making it prone to mistranslations due to speaking habits, dialects, background noise, and situations where multiple people are speaking simultaneously. Technical terms and abbreviations present particular challenges. Furthermore, AI transcription does not fully understand the context or nuances of speech, resulting in misinterpretations and unnatural sentence divisions. Sharing these mistranslations as is could lead to the spread of misinformation. Furthermore, when there are multiple speakers, it can be difficult to accurately identify who is speaking, so caution is required in situations where accurate recording is required. Furthermore, improper handling of audio and text data increases the risk of personal information leaks, so careful operation is required from a privacy perspective. Even automated transcription is not perfect, and manual editing may be necessary to correct misrecognitions and unnecessary phrases. Therefore, complete automation is still a long way off.

In other words, it is important to use it appropriately according to the purpose and situation, rather than using it as an "all-purpose tool."

Recommendation for hybrid operation with manual transcription

It is also recommended to use either human transcription (manual transcription) or a hybrid operation where automatic transcription is followed by human finalization, depending on the purpose and situation. Compared to automatic transcription, manual transcription can be expected to be more accurate because a human understands the context and nuances before writing. Another benefit is that technical terms, proper nouns, and abbreviations can be properly researched while transcribing, resulting in fewer errors. In addition to transcribing the content as is, it is also possible to edit it to make it easier to read, extract only the necessary parts, and create a free translation, summary, or minutes. In practice, a hybrid operation where "automatic transcription is first performed, followed by human finalization" is also common.

Onkyo offers a highly accurate transcription service that is difficult to achieve with automated transcription. We also meet the needs of fields that require accuracy, such as local government minutes, conferences, and English translations.

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How to Choose an Automatic Transcription Tool

When choosing an automatic transcription tool, it's important to consider not only ease of use, but also the purpose, environment, accuracy, and safety, as well as whether it supports both Japanese and English. When choosing an automatic transcription tool, it's important to first prioritize high recognition accuracy depending on the purpose and situation. For example, in situations where multiple people are speaking, such as meetings or interviews, speaker separation and the ability to handle technical terminology are essential.

In addition, there are significant differences in functionality depending on the tool, such as real-time transcription, support for uploading recorded files, translation functions, and output with timestamps.

Security is also an important consideration. When handling internal meetings or highly confidential audio, you need to make sure that the system has encryption and user authentication features. Additionally, pricing structures and supported devices (PCs, smartphones, cloud connectivity, etc.) are also important criteria for selection.

In other words, the key to selecting an automatic transcription tool is to strike a balance between accuracy, functionality, security, cost, and ease of use. By choosing a tool that suits your purpose, you can maximize the benefits of organizing information and improving work efficiency.

The future of automated transcription

Automatic transcription is undergoing significant change due to advances in AI technology. Advances in the latest speech recognition technology are expected to transform automatic transcription into an increasingly sophisticated and multifunctional core of information processing. In addition to its traditional function of converting speech to text, AI is now capable of speaker identification, context and emotion analysis, and even summarization and translation in real time. In the future, the integration of speech recognition and natural language processing will further advance, leading to the automatic summarization of transcribed text and its formatting as minutes and reports, as well as its use in business decision-making support and knowledge management. Furthermore, advances in multimodal AI are expected to enable the integrated analysis of not only speech but also non-verbal information such as video and gestures, enabling a richer understanding of communication. Automatic transcription is thus evolving from a simple recording tool into an "intellectual infrastructure" that maximizes the value of information, and is expected to become an indispensable part of all fields, including education, medicine, media, and business. Furthermore, advances in the latest speech recognition technology will enable automatic transcription to evolve into more flexible and advanced information utilization, linking devices, smartphones, and the cloud. In addition to traditional PCs and dedicated devices, apps that allow for easy recording and real-time transcription on smartphones are becoming more common, making it possible to instantly record content anywhere. Furthermore, by utilizing the cloud, audio data and text can be safely stored and seamlessly shared and edited across multiple devices, greatly accelerating information organization and work efficiency. Furthermore, cloud-based AI engines are now capable of advanced processing such as speaker identification, context understanding, and emotion analysis, evolving from a simple transcription of audio to an infrastructure that can be used across organizations as meaningful knowledge. Through the integration of devices, smartphones, and the cloud, automatic transcription is expected to become an essential information infrastructure in all workplaces.

summary

Automatic transcription plays a vital role as a tool that enables accurate recording and rapid utilization of information. In business settings in particular, real-time transcription of spoken content during meetings, interviews, presentations, and other events allows users to focus on the conversation without the need to take notes, and the generated text can also be used as meeting minutes and reports. In today's information society, automatic transcription goes beyond simply recording audio and plays a vital role in visualizing communication, streamlining information utilization, and accumulating knowledge. In other words, automatic transcription transforms "spoken information" into "utilizable information," becoming increasingly indispensable as an information infrastructure that boosts the productivity and creativity of organizations and individuals.