The Future of Audio Transcription: Emerging Technologies and Trends

by | Last updated May 27, 2023 | Published on May 16, 2023 | Audio Transcription, Digital Transcription

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Emerging Technologies and Trends in Audio Transcription

The process of converting spoken words from an audio recording into written text, known as audio transcription, has been in existence for a long time. Businesses use audio transcription services to create written records of conferences, meetings, interviews, and other important interactions. As the demand for these services grows, the emergence of new technologies is streamlining the process of converting recorded audio-visual files into text and improving efficiency.

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Emerging Technologies and Trends in Audio Transcription

Here are some of the emerging technologies and trends in audio transcription:

  • Artificial Intelligence (AI) and Machine Learning (ML): AI transcription involves the use of artificial intelligence to transcribe audio recordings. It is a complex process that involves a combination of advanced technologies and algorithms working together to analyze audio recordings and automatically transcribe them into written text. AI transcription can convert audio recordings into written text with high accuracy and speed. The process works as follows:
    • Audio input: The audio recording is uploaded to the AI transcription software, which analyzes the audio data.
    • Speech recognition: The software uses speech recognition algorithms to identify and distinguish individual words and phrases in the audio recording. Factors like speaker identification, accents, and background noise are taken into consideration.
    • Natural language processing (NLP): The software uses NLP techniques to interpret and understand the meaning of the words and phrases in context, and accurately transcribe the audio recording into coherent written text.
    • Machine learning (ML): AI transcription software features ML algorithms that enable it to learn and improve over time. As it transcribes more audio recordings, it learns to recognize patterns and improve its accuracy.

As computer algorithms are still not efficient when it comes to deciphering the nuances hidden in speech, the written transcript generated by the software should be edited and refined by a professional audio transcription service provider for further accuracy and clarity.

    • Natural Language Processing (NLP): A branch of AI, NLP technology empowers computers to comprehend and interpret human language in its spoken and written forms. NLP combines computer science and linguistics to develop systems capable of analyzing, processing, and extracting meaning from text and speech using advanced ML and deep learning algorithms. Automatic text translation is one of the many real-world applications of these systems. As NLP algorithms can recognize speech patterns, sentence structure, and even the speaker’s tone and emotions, it provides more accurate and relevant audio transcription.

The complex NLP process has two main components, natural language understanding (NLU), and natural language generation (NLG). NLP involves several steps such as:

      • Tokenization: The input text is broken down into tokens or smaller units, such as words or phrases.
      • Part-of-speech tagging: Each token is assigned a part-of-speech tag, such as noun, verb, or adjective.
      • Parsing: The relationships between the tokens are determined to create a parse tree representing the grammatical structure of the sentence.
      • Named entity recognition: Named entities in the text, such as people, organizations, and locations are identified and classified.
      • Sentiment analysis: The system determines the overall sentiment conveyed by the text, such as positive, negative, or neutral.
      • Coreference resolution: The system identifies and addresses references to entities that appear multiple times in the text, such as pronouns.
      • ML: NLP systems use machine learning algorithms to learn from large amounts of text data, which helps them improve their accuracy over time.

Computers can process and interpret large amounts of text data and gain insights from it by harnessing the power of NLP.

      • Automatic Speech Recognition (ASR): ASR is a technology that is used to automatically transcribe spoken words into text and can now accurately recognize accents, dialects, and even multiple speakers. Speech-to-text technology facilitates the translation of human speech from analog to digital form, which is then transcribed into an editable text format.

The process involves using an analog-to-digital converter to transform the input data into digital form, and applying linguistic algorithms to distinguish vibrations from auditory signals. The relevant sounds are filtered by measuring the sound waves and then segmented into hundredths or thousandths of seconds. These segments are matched against phonemes, which are measurable units of sound that differentiate one word from another. The phonemes are then subjected to a mathematical model that compares them with well-known words, sentences, and phrases. The output is available in a text or computer-based audio file.

Popular voice-to-text software include: Google Docs Voice Typing, Dragon Professional individual, Briana Pro, e-speaking, Speechnotes, Apple Dictation, and Windows Speech Recognition

      • Cloud-based Transcription: Cloud-based audio transcription services are becoming increasingly popular. The process begins with uploading the audio files to the cloud where they are automatically transcribed by a ML algorithm or a speech recognition engine. The user reviews the transcription and makes necessary corrections or edits. Some cloud-based transcription services also offer real-time editing while the audio or video is still playing. The final transcript is then delivered to the user via email or a cloud-based storage service, such as Dropbox or Google Drive. Cloud-based transcription makes the process faster and more convenient.
      • Mobile Apps: There are several mobile apps that allow users to record audio on their smartphones and then transcribe it on the go. Some of these apps even leverage AI and ML technologies to improve accuracy. Furthermore, these options come with extra features like text editing, and cloud-based storage and sharing of transcribed files. These mobile apps can be a handy and practical solution for students and journalists. When selecting a mobile app for transcription, consider the following factors:
        • As some apps restrict the time per session, verify the duration limit of the audio,
        • Check if the app is compatible with various platforms, especially iPhone, iPad, or Apple Watch.
        • If you plan to transcribe in a foreign language or accent that is different from your own, ensure that the application supports it.

Popular mobile apps for transcription include:, Rev Voice Recorder, Dragon Anywhere, TranscribeMe, and Speechnotes.

Check Automated Transcripts for Accuracy and Completeness

Although automated transcription is efficient, convenient, and time-saving, it can cause errors in the transcribed text. That’s why it’s important to have human transcribers check the automated transcripts for accuracy and completeness. Many factors can affect the accuracy of automated transcripts:

      • Automated transcripts can have flaws when the audio quality is poor, or there is background noise.
      • The technology can sometimes misinterpret the context, leading to incorrect transcripts.
      • Complex or technical language, jargon, or industry-specific terms often pose challenges for automated transcription technology.
      • The technology may not format the text correctly, such as paragraph breaks, headings, or bullet points

Although automated transcription technology has made audio transcription faster, it is not always 100% accurate. Having your automated transcripts checked by a digital transcription service provider that has skilled human transcriptionists can ensure high quality transcripts that meet the desired standards. Nevertheless, as technologies continue to evolve, we can expect to see more improvements in the field of audio transcription.

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