#automated transcription

[transcriptionservicephiladelphia]

The Pitfalls of AI: Common Errors in Automated Transcription and How Professionals Avoid Them

Automated transcription software has revolutionized how we process audio and video into text. Powered by artificial intelligence, these tools offer quick and affordable solutions for converting spoken content into written text. However, as convenient as they may be, automated transcription tools have their challenges. AI transcription often falls short of professional standards, from misinterpreting accents to struggling with background noise. In this article, we’ll explore some common automated transcription errors in automated transcription and how professional transcriptionists skillfully avoid them.

Misinterpretation of Accents and Dialects

One of the biggest challenges in automated transcription is accurately interpreting accents and dialects. AI transcription tools are generally trained on a limited set of accents, often those most prevalent in North American or British English. This can result in misinterpretations when speakers have regional accents or use unique dialectal expressions, leading to numerous errors in the final transcript.

How Professionals Avoid It: Skilled transcriptionists are trained to recognize and understand a variety of accents and dialects. They can discern context accurately to produce a clear, accurate transcript. They may also use clarification techniques, such as replaying specific audio parts, to ensure accuracy.

Struggles with Background Noise and Overlapping Speech

AI transcription tools often falter when background noise or overlapping speech is present. External sounds can easily confuse automated transcription, whether it’s the hum of machinery, chatter in a crowded room, or multiple people talking at once. The result? Jumbled text with random words inserted or critical points left out altogether.

How Professionals Avoid It: In-house transcription uses high-quality audio equipment and may employ specialized software to filter out background noise. They also listen carefully to distinguish between different voices in overlapping speech, ensuring that each person’s statements are accurately recorded.

Incorrect Homophones and Contextual Mistakes

AI transcription systems often struggle with homophones—words that sound the same but have different meanings (e.g., “there,” “their,” and “they’re”). Because AI tools lack a nuanced understanding of context, they can misinterpret these words, resulting in sentences that may look correct but are nonsensical in context.

How Professionals Avoid It: Human transcriptionists use contextual understanding to identify the correct homophone or term in each case. They understand nuances and can catch these errors based on the meaning and intent of the conversation, ensuring clarity and coherence.

Misinterpretation of Industry-Specific Terms and Jargon

Automated transcription tools can need help with industry-specific terminology and jargon, especially in specialized fields like medicine, law, or finance. AI software may not recognize complex terms or technical phrases, leading to incorrect transcriptions that don’t make sense within the professional context.

How Professionals Avoid It: Professionals trained in specific industries have the background knowledge to identify and correctly transcribe technical terms. They often consult glossaries or terminology resources, ensuring the transcript accurately captures specialized language.

Lack of Punctuation and Formatting

Automated transcription often needs to improve punctuation and formatting. AI tools frequently produce a single text block without punctuation, paragraph breaks, or speaker labels, making the transcript hard to read or follow. Inaccurate punctuation can also change the meaning of sentences, leading to misunderstandings.

How Professionals Avoid It: Human transcriptionists pay close attention to natural speech pauses and cues, using punctuation to maintain the intended meaning. They also structure the transcript in an organized way, adding speaker labels and creating a readable document that captures the flow of the conversation.

Challenges with Complex Sentence Structures

AI transcription struggles with complex sentence structures, often breaking sentences at the wrong points or connecting unrelated clauses. This can distort the meaning of the content and make it difficult for readers to follow.

How Professionals Avoid It: Skilled transcriptionists can parse complex sentences and retain the original meaning. They listen carefully to ensure that clauses are accurately connected, producing a natural and understandable flow.

Difficulty with Non-Standard Speech Patterns

Non-standard speech patterns, such as stuttering, filler words (“uh,” “um”), or abrupt topic changes, can confuse AI transcription tools. These systems may omit or overrepresent filler words, leading to too-polished or overly disjointed transcripts.

How Professionals Avoid It: Human transcriptionists use judgment to decide whether to include or omit fillers. They can adjust to ensure clarity while preserving the speaker’s natural tone and intent.

Limited Support for Multiple Languages and Code-Switching

AI transcription tools often falter in multilingual contexts or instances of code-switching (switching between languages). They may only transcribe portions in the language they are trained for, ignoring or misinterpreting other languages.

How Professionals Avoid It: Bilingual transcriptionists with language expertise can seamlessly transcribe code-switching conversations. They ensure that the correct language is captured, keeping the context intact.

How Professionals Avoid AI Transcription Pitfalls

Despite these common AI transcription pitfalls, professional transcriptionists bring human expertise and skill to produce high-quality transcripts. Here’s a quick look at some methods they use:

Active Listening: Professionals listen intently, picking up on nuances and contexts that AI often misses.

Quality Control: They review transcripts for accuracy, correcting mistakes that automated software might leave in.

Specialized Knowledge: In medical or legal transcription, professionals use their industry knowledge to interpret jargon and technical terms.

Precision Editing: Trained transcriptionists use proper punctuation, paragraph breaks, and speaker labeling to create clear, easy-to-read documents.

Noise Reduction Techniques: Using advanced audio software, professionals can isolate voices from background noise, which improves accuracy.

Continuous Learning: Human transcriptionists stay updated with new language trends, industry terms, and best practices, ensuring they deliver the most reliable results.

Automated transcription tools are handy, but they have limitations. From dealing with accents and background noise to understanding context and technical jargon, AI transcription still needs to improve. Professional transcriptionists bring a level of accuracy and context that automated tools simply can’t replicate. They enhance the quality of transcriptions by leveraging human insight, expertise, and attention to detail—ensuring that the final product is accurate but also polished and professional.

When clarity, accuracy, and professionalism are essential, hiring a professional transcriptionist is the best way to avoid common AI pitfalls and achieve a reliable, high-quality transcript.

[ai-innova7ions]

Rask AI: The Socratic Lab That Will Change Education Forever

Discover how Rask AI enhances accuracy in transcription, seamlessly converting audio to text with automated transcription technology. This means better accessibility and inclusivity in education, allowing for a more engaging learning environment for everyone. With its creative solutions, Rask AI is set to redefine media interaction, providing multilingual content that caters to diverse audiences.

[thatimageoftomscott]

I've been rawdogging lateral this whole time??? They have transcripts??? I couldn't find them when I started listening and so I've been struggling

Thank you so much

yes ! they are not on spotify yet because spotify doesnt support transcripts like that apparently but they are trying to make them fix it. full transcripts are always on the lateral site and they are pretty great.

[bastart13]

Combining work coding with my hobbies is the best thing! I've learnt how to extract text from images/video and I've managed to transcribe ~10 Love & Legends episodes in less than a day :D

[cepheusgalaxy]

[Image descriptions: Multiple five-tripe color pallettes with the hexadecimal code and name of each color at the bottom of them, in vertical, from left to right: 1. The colors of the trans flag: 55CDFC vivid sky blue, F7A8B8 cherry blossom pink, FFFFFF white, F7A8B8 cherry blossom pink and 55CDFC vivid sky blue; 2. 8BB0AD morning blue, E3A28A dark salmon, F3EEEC isabelline, E3A28A dark salmon and 8BB0AD morning blue; 3. B0FFE1 magic mint, F7A8A8 pastel pink, FFFAEE floral white, F7A8A8 pastel pink and B0FFE1 magic mint; 4. ACA4FF maximum blue purple, FF99B8 baker miller pink, FEFFD8 light goldenrod yellow, FF99B8 baker miller pink and ACA4FF maximum blue purple; 5. 0400FF blue, FF00E1 hot magenta, DDF4FF azure x 11 webcolor, FF00E1 hot magenta and 0400FF blue; 6. 7997B8 cerulean frost, D1638F china pink, F2EDE1 alabaster, D1638F china pink and 7997B8 cerulean frost; 7. 5838C9 ocean blue, D46798 wild orchid, BEC3DC periwinkle crayola, D46798 wild orchid and 5838C9 ocean blue; 8. C4D6D6 opal, C2A3B1 liliac luster, EDECED platinum, C2A3B1 liliac luster and C4D6D6 opal; 9. C6CFFF lavender blue, FFC9FA cotton candy, FFF2FD lavender blush, FFC9FA cotton candy and C6CFFF lavender blue. /end ID.]

original

salmon and bonito flakes

roller skates

floral perfume

rawr

busted up converse sneakers

grape cherry cola in the evening

dead bouquet

at the fair at dusk with candyfloss

messing around with colour pallettes for the tramsgener flag, reblogs appreciated ((: free to use for whatever, i'd love to see what you do with them!!

gonna do uhhhhhghffdhvbh non binary flag next or maybe bi or pan idk

[precallai]

Integrating AI Call Transcription into Your VoIP or CRM System

In today’s hyper-connected business environment, customer communication is one of the most valuable assets a company possesses. Every sales call, support ticket, or service request contains rich data that can improve business processes—if captured and analyzed properly. This is where AI call transcription becomes a game changer. By converting voice conversations into searchable, structured text, businesses can unlock powerful insights. The real value, however, comes when these capabilities are integrated directly into VoIP and CRM systems, streamlining operations and enhancing customer experiences.

Why AI Call Transcription Matters

AI call transcription leverages advanced technologies such as Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) to convert real-time or recorded voice conversations into text. These transcripts can then be used for:

Compliance and auditing

Agent performance evaluation

Customer sentiment analysis

CRM data enrichment

Automated note-taking

Keyword tracking and lead scoring

Traditionally, analyzing calls was a manual and time-consuming task. AI makes this process scalable and real-time.

Key Components of AI Call Transcription Systems

Before diving into integration, it’s essential to understand the key components of an AI transcription pipeline:

Speech-to-Text Engine (ASR): Converts audio to raw text.

Speaker Diarization: Identifies and separates different speakers.

Timestamping: Tags text with time information for playback syncing.

Language Modeling: Uses NLP to enhance context, punctuation, and accuracy.

Post-processing Modules: Cleans up the transcript for readability.

APIs/SDKs: Interface for integration with external systems like CRMs or VoIP platforms.

Common Use Cases for VoIP + CRM + AI Transcription

The integration of AI transcription with VoIP and CRM platforms opens up a wide range of operational enhancements:

Sales teams: Automatically log conversations, extract deal-related data, and trigger follow-up tasks.

Customer support: Analyze tone, keywords, and escalation patterns for better agent training.

Compliance teams: Use searchable transcripts to verify adherence to legal and regulatory requirements.

Marketing teams: Mine conversation data for campaign insights, objections, and buying signals.

Step-by-Step: Integrating AI Call Transcription into VoIP Systems

Step 1: Capture the Audio Stream

Most modern VoIP systems like Twilio, RingCentral, Zoom Phone, or Aircall provide APIs or webhooks that allow you to:

Record calls in real time

Access audio streams post-call

Configure cloud storage for call files (MP3, WAV)

Ensure that you're adhering to legal and privacy regulations such as GDPR or HIPAA when capturing and storing call data.

Step 2: Choose an AI Transcription Provider

Several commercial and open-source options exist, including:

Google Speech-to-Text

AWS Transcribe

Microsoft Azure Speech

AssemblyAI

Deepgram

Whisper by OpenAI (open-source)

When selecting a provider, evaluate:

Language support

Real-time vs. batch processing capabilities

Accuracy in noisy environments

Speaker diarization support

API response latency

Security/compliance features

Step 3: Transcribe the Audio

Using the API of your chosen ASR provider, submit the call recording. Many platforms allow streaming input for real-time use cases, or you can upload an audio file for asynchronous transcription.

Here’s a basic flow using an API:

python

CopyEdit

import requests

response = requests.post(

    "https://api.transcriptionprovider.com/v1/transcribe",

    headers={"Authorization": "Bearer YOUR_API_KEY"},

    json={"audio_url": "https://storage.yourvoip.com/call123.wav"}

)

transcript = response.json()

The returned transcript typically includes speaker turns, timestamps, and a confidence score.

Step-by-Step: Integrating Transcription with CRM Systems

Once you’ve obtained the transcription, you can inject it into your CRM platform (e.g., Salesforce, HubSpot, Zoho, GoHighLevel) using their APIs.

Step 4: Map Transcripts to CRM Records

You’ll need to determine where and how transcripts should appear in your CRM:

Contact record timeline

Activity or task notes

Custom transcription field

Opportunity or deal notes

For example, in HubSpot:

python

CopyEdit

requests.post(

    "https://api.hubapi.com/engagements/v1/engagements",

    headers={"Authorization": "Bearer YOUR_HUBSPOT_TOKEN"},

    json={

        "engagement": {"active": True, "type": "NOTE"},

        "associations": {"contactIds": [contact_id]},

        "metadata": {"body": transcript_text}

    }

)

Step 5: Automate Trigger-Based Actions

You can automate workflows based on keywords or intent in the transcript, such as:

Create follow-up tasks if "schedule demo" is mentioned

Alert a manager if "cancel account" is detected

Move deal stage if certain intent phrases are spoken

This is where NLP tagging or intent classification models can add value.

Advanced Features and Enhancements

1. Sentiment Analysis

Apply sentiment models to gauge caller mood and flag negative experiences for review.

2. Custom Vocabulary

Teach the transcription engine brand-specific terms, product names, or industry jargon for better accuracy.

3. Voice Biometrics

Authenticate speakers based on voiceprints for added security.

4. Real-Time Transcription

Show live captions during calls or video meetings for accessibility and note-taking.

Challenges to Consider

Privacy & Consent: Ensure callers are aware that calls are recorded and transcribed.

Data Storage: Securely store transcripts, especially when handling sensitive data.

Accuracy Limitations: Background noise, accents, or low-quality audio can degrade results.

System Compatibility: Some CRMs may require custom middleware or third-party plugins for integration.

Tools That Make It Easy

Zapier/Integromat: For non-developers to connect transcription services with CRMs.

Webhooks: Trigger events based on call status or new transcriptions.

CRM Plugins: Some platforms offer native transcription integrations.

Final Thoughts

Integrating AI call transcription into your VoIP and CRM systems can significantly boost your team’s productivity, improve customer relationships, and offer new layers of business intelligence. As the technology matures and becomes more accessible, now is the right time to embrace it.

With the right strategy and tools in place, what used to be fleeting conversations can now become a core part of your data-driven decision-making process.

[gremlinshatephilosophers]

Some long notes for this one but hey, it was a long fic that I wrote over a long time. If anyone's curious about the thought process behind Lying Makes You Go to Hell, here it is:

[avertigo]

10 Artificial Intelligence tools to increase your productivity

Do you think Artificial Intelligence (AI) is only for programmers or engineers? Error! AI is a tool that is within everyone’s reach, and if you don’t already use it because you think it’s “complicated”, you’re falling behind. In a world where technology is advancing by leaps and bounds, not taking advantage of these tools can put you at a disadvantage. But don’t worry, you don’t need to be…

[aiwikiweb]

Streamline Your Content Creation with Exemplary AI: The Ultimate Repurposing Toolkit

Exemplary AI is an innovative platform designed to transform your long-form videos and audios into engaging, shareable content. By leveraging advanced AI algorithms, it automates the creation of transcripts, subtitles, clips, blog posts, and more, enabling you to maximize the reach and impact of your content.

Core Functionality:

Exemplary AI simplifies the content repurposing process by converting extensive videos and audios into concise, audience-friendly formats. This approach ensures that your content resonates across various platforms and formats.

Key Features:

Automatic Transcription: Generates accurate transcripts of your audio and video files, facilitating easy editing and content extraction.

Subtitle Generation: Creates subtitles in over 120 languages, enhancing accessibility and engagement.

AI-Powered Clips: Identifies and extracts key moments to produce short, impactful clips suitable for social media sharing.

Blog Post Creation: Transforms your content into well-structured blog posts, expanding your content's reach.

Audiogram Production: Converts audio content into visually engaging videos with dynamic subtitles and sound waves.

Benefits:

Efficiency: Automates time-consuming tasks, allowing you to focus on content strategy and creation.

Consistency: Ensures uniformity across different content formats, maintaining your brand's voice and style.

Scalability: Enables you to produce a high volume of content without compromising quality.

Elevate your content strategy with Exemplary AI. Visit aiwikiweb.com/product/exemplary

[infinicrew]

>AUTO-TRANSCRIPT SAVED RECORD

>DATE: 20XX-03-25, 09:32 UTC

>LOCATION: MECHANICAL, GROUND FLOOR

>BEGIN TRANSCRIPT>>>

Octave trips on some wires.

OCTAVE: owugh...

>END TRANSCRIPT>>>

[transcriptioncity]

Transcription Services for Accessibility and Inclusion

The Importance of Transcription Services for Accessibility and Inclusion In the digital age, 15% of the global population experiences some form of disability. This significant statistic underscores the critical need for accessibility. Transcription services play a pivotal role in enhancing accessibility and inclusion. This article delves into how transcription services help make digital content…

[mr-hyde-on-the-move]

Virtual Humans on Demand: The Rise of AI Talkie in Video Marketing | With 6 Bonuses

In the ever-evolving landscape of digital marketing, a groundbreaking application has emerged, promising to transform the way we create and monetize videos. Meet AI Talkie, the first-to-market app that generates AI-human videos effortlessly, garnering impressive views and translating them into automatic commissions. In this review, we’ll explore the key features, benefits, and potential drawbacks…

[bastart13]

sending you strength and good vibes as you work through s3 of l&l. re: your post i think one of the strengths/weaknesses of the whole lovestruck series is how isolated the routes were in terms of character development (which was great for the writers having to make these- maybe not great for everyone reading more than one route). i've noticed it gets pretty wacky!

I think on the whole the routes in any given story are pretty consistent, but it's also a coin-flip if a character you like is sent in a completely different direction. Sometimes the narrative they're given makes a weird choice that throws it off, sometimes the writing just focuses on a different aspect of their character than you expected

[reasonsforhope]

"Canadian scientists have developed a blood test and portable device that can determine the onset of sepsis faster and more accurately than existing methods.

Published today [May 27, 2025] in Nature Communications, the test is more than 90 per cent accurate at identifying those at high risk of developing sepsis and represents a major milestone in the way doctors will evaluate and treat sepsis.

“Sepsis accounts for roughly 20 per cent of all global deaths,” said lead author Dr. Claudia dos Santos, a critical care physician and scientist at St. Michael’s Hospital. “Our test could be a powerful game changer, allowing physicians to quickly identify and treat patients before they begin to rapidly deteriorate.”

Sepsis is the body’s extreme reaction to an infection, causing the immune system to start attacking one’s own organs and tissues. It can lead to organ failure and death if not treated quickly. Predicting sepsis is difficult: early symptoms are non-specific, and current tests can take up to 18 hours and require specialized labs. This delay before treatment increases the chance of death by nearly eight per cent per hour.

[Note: The up to 18 hour testing window for sepsis is a huge cause of sepsis-related mortality, because septic shock can kill in as little as 12 hours, long before the tests are even done.]

[Analytical] AI helps predict sepsis

Examining blood samples from more than 3,000 hospital patients with suspected sepsis, researchers from UBC and Sepset, a UBC spin-off biotechnology company, used machine learning to identify a six-gene expression signature “Sepset” that predicted sepsis nine times out of 10, and well before a formal diagnosis. With 248 additional blood samples using RT-PCR, (Reverse Transcription Polymerase Chain Reaction), a common hospital laboratory technique, the test was 94 per cent accurate in detecting early-stage sepsis in patients whose condition was about to worsen.

“This demonstrates the immense value of AI in analyzing extremely complex data to identify the important genes for predicting sepsis and writing an algorithm that predicts sepsis risk with high accuracy,” said co-author Dr. Bob Hancock, UBC professor of microbiology and immunology and CEO of Sepset.

Bringing the test to point of care

To bring the test closer to the bedside, the National Research Council of Canada (NRC) developed a portable device they called PowerBlade that uses a drop of blood and an automated sequence of steps to efficiently detect sepsis. Tested with 30 patients, the device was 92 per cent accurate in identifying patients at high risk of sepsis and 89 per cent accurate in ruling out those not at risk.

“PowerBlade delivered results in under three hours. Such a device can make treatment possible wherever a patient may be, including in the emergency room or remote health care units,” said Dr. Hancock.

“By combining cutting-edge microfluidic research with interdisciplinary collaboration across engineering, biology, and medicine, the Centre for Research and Applications in Fluidic Technologies (CRAFT) enables rapid, portable, and accessible testing solutions,” said co-author Dr. Teodor Veres, of the NRC’s Medical Devices Research Centre and CRAFT co-director. CRAFT, a joint venture between the University of Toronto, Unity Health Toronto and the NRC, accelerates the development of innovative devices that can bring high-quality diagnostics to the point of care.

Dr. Hancock’s team, including UBC research associate and co-author Dr. Evan Haney, has also started commercial development of the Sepset signature. “These tests detect the early warnings of sepsis, allowing physicians to act quickly to treat the patient, rather than waiting until the damage is done,” said Dr. Haney."

-via University of British Columbia, May 27, 2025

[that-bat]

Thinking about this clip from november 2022 where Tango talks about how cool it would be to play plate-up in minecraft (specifically create mod). And then two years later he actually does it by making hungry hermits

Transcript:

Tango, reading from chat: "It [the farmer's delight mod] adds crops, foods, cooking blocks, you can build a real kitchen"

Tango: "Can I play plate-up in minecraft? That's the real question. I suppose with create mod I could. Can I automate my kitchen? Burger up! That would be amazing"

Tango, reading from chat: "Plate-up for create!"

Tango: "I know shawshank, right?"

[clip skips ahead a few minutes]

Tango: "That would be amazing, to make plate-up in minecraft"