#AssemblyAI

[aiwikiweb]

Could AI-Powered Transcription Replace Human Transcribers?

Imagine a future where AI tools like AssemblyAI handle all transcription tasks. Could AI fully replace human transcribers, or are there certain nuances that only a human can understand?

Scenario: Consider a future where businesses, content creators, and media companies rely entirely on AI to transcribe audio and video content. AssemblyAI and similar platforms can provide accurate, real-time transcriptions at scale, eliminating the need for human transcribers. The role of transcription may shift from manual typing to reviewing and editing AI-generated transcripts.

Analysis:

Potential Benefits:

Speed and Efficiency: AI can transcribe content in minutes, allowing for fast turnaround times that are difficult for human transcribers to match.

Cost Savings: Using AI for transcription can reduce costs associated with hiring transcribers, making transcription services accessible to more individuals and businesses.

Challenges:

Understanding Context: Human transcribers can understand the context of conversations, accurately transcribing difficult accents, slang, or industry-specific terminology. Would AI struggle with these nuances, leading to errors in certain cases?

Complex Audio Quality: In cases of poor audio quality, background noise, or overlapping conversations, human transcribers might perform better in distinguishing and accurately transcribing speech.

Do you think AI transcription tools could fully replace human transcribers, or are there certain nuances that only a human can understand? Would you trust AI to handle all your transcription needs? Share your thoughts!

Join the conversation on the future of transcription. Could AI fully replace human transcribers, or will the human touch always be needed for certain tasks?

Share your views and explore more at aiwikiweb.com/product/assembly-ai/

[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.

[3acesnews]

Supernormal Boosts Conversion Rates with AssemblyAI's Enhanced Transcription

[manukyan]

AssemblyAI Campaign Launch from Good Secrets on Vimeo.

[the-latest-research]

Speech and Voice Recognition Market to be Worth $56.07 Billion by 2030

Meticulous Research®—leading global market research company, published a research report titled, ‘Speech and Voice Recognition Market by Function (Speech, Voice Recognition), Technology (AI and Non-AI), Deployment Mode (Cloud, On-premise), End User (Consumer Electronics, Automotive, BFSI, Other End Users), and Geography - Global Forecast to 2030.’

Speech Recognition Market Booming with AI and Growing Applications

The speech recognition market is poised for significant growth, reaching an estimated $56.07 billion by 2030 at a CAGR of 19.1%, acoording to Meticulous Research®. This surge is fueled by several key trends:

Voice Biometrics on the Rise: Security systems and financial applications are increasingly adopting voice biometrics for user authentication, offering a convenient and secure solution.

Voice Assistants Take Center Stage: Virtual assistants powered by AI are transforming how we interact with technology in homes, cars, and workplaces.

Smart Devices Drive Demand: The proliferation of voice-enabled smart speakers, wearables, and appliances is creating a strong demand for accurate speech recognition technology.

Download Sample Report Here @ https://www.meticulousresearch.com/download-sample-report/cp_id=5038

Challenges and Opportunities in Speech Recognition

Despite its growth potential, the market faces some hurdles:

Accent and Dialect Hurdles: Current systems may struggle with regional variations in speech patterns, requiring ongoing development for wider adoption.

Background Noise Interference: Speech recognition accuracy can be hampered by ambient noise, demanding improvements in noise cancellation techniques.

However, exciting opportunities lie ahead:

AI Integration Enhances Functionality: The integration of Artificial Intelligence and Machine Learning is continuously improving speech recognition accuracy and functionality.

Multilingual Communication: Speech recognition is poised to play a vital role in bridging language barriers by facilitating translation of rare and local languages.

Voice Authentication Gains Traction: The growing demand for secure mobile banking and other applications is driving the adoption of voice authentication technologies.

Market Segmentation Highlights

The report also explores various segments within the speech recognition market:

Function: Speech recognition (converting speech to text) holds the dominant market share due to the widespread use of AI and smart devices.

Technology: AI-powered speech recognition is leading the way due to its effectiveness in powering virtual assistants and other intelligent applications.

Deployment Mode: Cloud-based deployments are gaining traction due to their scalability, affordability, and ease of use, particularly for small and medium businesses.

End User: The IT and telecommunications sector currently holds the largest share, but the consumer electronics segment is expected to witness the fastest growth due to the rising popularity of voice-enabled devices.

Geography: North America dominates the market due to the presence of major technology players and a strong focus on improving customer service experiences.

By understanding these trends and segmentation, businesses can capitalize on the immense potential of the speech recognition market.

Key Players:

Some of the key players operating in the speech and voice recognition market are Microsoft Corporation (U.S.), Amazon Web Services, Inc. (U.S.), Google LLC (U.S.), IBM Corporation (U.S.), Verint Systems Inc. (U.S.), Baidu, Inc. (China), Apple Inc. (U.S.), Speechmatics (U.K.), Sensory, Inc. (U.S.), AssemblyAI, Inc. (U.S.), iFLYTEK Co., Ltd. (China), LumenVox (U.S.), SESTEK (Turkey), and Dolbey Systems, Inc. (U.S.).Contact Us: Meticulous Research® Email- sales@meticulousresearch.com Contact Sales- +1-646-781-8004 Connect with us on LinkedIn- https://www.linkedin.com/company/meticulous-research

[adi-barda]

Chapter 2 - Gemini API Developer Competition

The last few days were intense. I needed to have a complete flow running with consistent results. Last time I created the plug-ins system for the SceneMax3D platform and an in-process webserver plugin for accepting commands from the outside world into the running game and modify it on-the-fly. This time, I was focused on creating a set of plugins for the SceneMax3D IDE as follows:

Speech To Text - I decided to use AssemblyAI as my speech to text provider. They provide an extremely easy to use speech to text Java SDK and registration process along with some nice features and a reasonable pricing model. For the speech to text feature, I decided to use the real-time streaming model. It was the coolest and easiest to implement.

Prompt Manager - the text provided by the speech-to-text plugin was handed to a prompt manager component which produced a well constructed and formatted prompt for Gemini AI including system instruction and user prompt.

Gemini AI API - the after mentioned prompt was delivered to a Gemini API component which is responsible for sending it to the Gemini AI service and return its result as plain text.

Configuration to code converter - the Gemini AI JSON text result is passed to a configuration to code converter which reproduces a valid SceneMax3D code. The code converter component has a built-in specific code converters for all kind of game genres such as racing games, fighting games, empty unknown games etc.

Gemini SceneMax3D Integration - this component is responsible for managing the entire workflow, orchestrating the various components described above from the user triggering the speech recognition and up to the generated SceneMax3D code which is sent to the run-time engine in real-time.

What's next

Now, that we have a complete working flow from the user interaction to the run-time game engine, we need to add the following features:

Enhance to configuration to code converter - support as much as possible user requests scenarios from updating a running scene up to switching to a new game concept while supporting as much as possible different game types.

Enhance the "Wow Factor" - make it look as impressive as possible emphasizing the Gemini AI contribution for the success of this process.

Export to Android devices - this is a requirement for this competition to have a Gemini AI product running on Android devices

Create a 3 minutes video clip demonstrating the product's capabilities.

[jhavelikes]

AssemblyAI is launching Universal-1, our most capable and highly trained speech recognition model. Trained on over 12.5 million hours of multilingual audio data, Universal-1 achieves best-in-class speech-to-text accuracy, reduces word error rate and hallucinations, improves timestamp estimation, and helps us continue to raise the bar as the industry-leading Speech AI provider. Universal-1 is trained on four major languages: English, Spanish, French, and German, and shows extremely strong speech-to-text accuracy in almost all conditions, including heavy background noise, accented speech, natural conversations, and changes in language, while achieving fast turn-around time and improved timestamp accuracy.

Introducing Universal-1

[techrookies]

AssemblyAI lands $50M to build and serve AI speech models

Companies are betting big on generative AI to gain a competitive edge. But adoption challenges remain. According to a recent survey from EY, a significant portion of businesses looking to embrace generative AI say that the field’s rapid progress — and the surge in vendors claiming to have AI expertise — is complicating their deployment […] © 2023 TechCrunch. All rights reserved. For personal use…

[futureailist]

AssemblyAI : Unlocking the Power of AI Speech Understanding with AssemblyAI Revolutionizing Speech Transcription and Analysis for Modern Applications In the rapidly evolving landscape of artificial intelligence, AssemblyAI stands as a pioneering force, offering a gateway to seamlessly integrate the power of AI-driven speech recognition and understanding into diverse applications. With a robust API providing access to cutting-edge models, AssemblyAI is reshaping the way we interact with audio data. In this article, we delve into the remarkable capabilities, real-world implications, and industry impact of Assembly AI. AssemblyAI : Revolutionizing the Way We Process Audio AssemblyAI's Product Portfolio AssemblyAI's product range encompasses a spectrum of AI-driven solutions, designed to tackle the complexities of speech understanding. Key offerings include: Speech Recognition: AssemblyAI's Conformer-2 model, the latest addition to their repertoire, boasts human-level accuracy in speech recognition. This state-of-the-art model outperforms other ASR models, displaying up to 43% fewer errors in noisy environments. Transcription: With the capability to transcribe audio in real-time, Assembly AI simplifies the conversion of spoken content into text. Their API goes beyond mere transcription, providing speaker labels, word-level timestamps, profanity filtering, custom vocabulary, and various other features for enhanced analysis. Audio Intelligence Models: Assembly AI offers a range of AI models for diverse applications, including summarization, sentiment analysis, and content moderation. Additionally, their new framework, LeMUR, empowers developers to build language model-powered applications on spoken data with unparalleled ease. Enterprise Scale: AssemblyAI's API is built to handle the demands of enterprise-level applications. With an impressive uptime of over 99.9% and SOC 2 Type 2 compliance, the platform ensures seamless and secure integration into various business ecosystems. Unleashing the Potential: LeMUR and Conformer-2 AssemblyAI's commitment to innovation is exemplified by their recent launches: Conformer-2: This state-of-the-art speech recognition model is trained on a staggering 1.1 million hours of data. Its accuracy and resilience in handling real-world complexities position it as a game-changer in the field of speech understanding. LeMUR: Assembly AI introduces LeMUR, a revolutionary framework for building Large Language Model (LLM)-powered applications using spoken data. This framework opens the doors to new realms of creativity and functionality, allowing developers to harness the power of LLMs without the traditional complexities. Empowering Businesses with AI Trusted by Industry Leaders AssemblyAI's impact resonates across industries and organizations of all sizes. Trusted by companies such as CallRail, Grain, and Aloware, the platform has demonstrated its prowess in enhancing customer experiences, generating insights, and enabling intelligent automation. From Developers, For Developers The platform's developer-centric approach fosters an environment of innovation and collaboration. Developers can effortlessly incorporate AI-powered features into their applications, automating tasks like auto-generating subtitles, summarizing audio content, identifying speakers, and more. AssemblyAI's API documentation serves as a comprehensive resource, aiding developers in harnessing the full potential of the platform. A Glance at the Pioneers Several voices from the tech community have applauded AssemblyAI's transformative capabilities: Prayag Narula, CEO & Co-Founder of Marvin, highlights Assembly AI as an indispensable tool for audio and video intelligence, emphasizing its pivotal role in their operations. Chanin Nantasenamat, a technology enthusiast, expresses excitement about AssemblyAI's Conformer-1 ASR model, illustrating the anticipation among tech enthusiasts to explore the potential of this technology. Matt

Blake, an experienced user, attests to AssemblyAI's superiority over other solutions in the market. The platform's ability to handle larger videos with impressive accuracy is noted as a significant advantage. Jonathan Stern's experimentation showcases Assembly AI's supremacy in transcription accuracy, as his comparison highlights AssemblyAI's performance in different scenarios. Seamless Integration: Try it Yourself ASR and LeMUR Playgrounds Assembly AI offers interactive playgrounds to experience the capabilities of their AI models firsthand: ASR Playground: Explore AI models in action across transcription, content moderation, sentiment analysis, and more. Witness the power of AI-driven speech understanding in real-time scenarios. LeMUR Playground: Dive into the LeMUR API to generate custom summaries, answers to questions, or feedback on lengthy audio files. Experience the ease of building LLM-powered applications. A Bright Future with AssemblyAI The realm of AI-driven speech understanding has found its trailblazer in Assembly AI. From revolutionizing transcription accuracy to enabling developers to build LLM-powered applications with ease, the platform's impact is undeniable. With a dedication to human-level accuracy, robust features, and a commitment to industry compliance, Assembly AI is poised to shape the future of speech understanding in the digital era. Join over 90,000 developers in embracing this transformative technology and redefine the way we interact with audio data.

[globaljobalert-blog]

Software Engineer, Research - Remote

Company: AssemblyAI AssemblyAI is a remote-first AI company building powerful deep learning models for developers, startups, and enterprises to transcribe and understand their audio data. Our Automated Speech Recognition (ASR) models already outperform companies like Google, AWS, and Microsoft - which is why hundreds of companies and thousands of developers are using our APIs to transcribe and understand millions of videos, podcasts, phone calls, and zoom meetings every day. Our APIs power innovative products like conversational intelligence platforms, zoom meeting summarizers, content moderation, and automatic closed captioning. AssemblyAI’s Speech-to-Text APIs are already trusted by Fortune 500s, startups, and thousands of developers around the world, with well-known customers including Spotify, Algolia, Dow Jones, Happy Scribe, BBC, The Wall Street Journal, and NBCUniversal. As part of a huge and emerging market, AssemblyAI is well on its way to becoming the leader in speech recognition and NLP. We're growing at breakneck speed, and recently announced our Series B round. We've raised $63M in total funding, and are backed by leading investors including Insight Partners, Accel, Y Combinator, Patrick and John Collision (Founders of Stripe), Nat Friedman (Former CEO of GitHub), and Daniel Gross (Entrepreneur & Investor in companies including GitHub, Uber & SpaceX)! Our ambition is to build an iconic AI company, making advanced deep learning technology accessible to everyday developers through a simple API, good docs, and a great developer experience. Join our world-class, remote team and help us build an iconic deep learning company! The Role AssemblyAI is growing quickly, and we’re searching for a mid-level software engineer to help create and own our Deep Learning research framework. You'll need strong software and cloud engineering skills and experience building maintainable systems. Collaboration skills will be important, as you will collaborate closely with the adjacent Research team and help direct a small team to complete larger projects. Some of your responsibilities will include: - Help to design our new experiment framework and integrate it with an open source management platform - Enable researchers to launch many experiments in the cloud across 100s of accelerators by running a single shell script - Design, implement, and maintain the experiment framework, databases, and documentation that all our researcher depend on everyday to perform research - Ensure that model code is hermetically packaged so that it can be easily deployed to production - Ensure that the platform is well tested and resilient to failures, capacity issues, etc. You'll love this job if you.... - Enjoy solving complex technical problems, even when there is no perfect solution - Enjoy building platforms, that evolve over time and scale other teams - Enjoy having ownership of a mission critical software - Enjoy working on a system that enables large scale deep learning research - Thrive in small, cross-functional teams. We like to wear many hats here! Requirements - 3+ years of engineering backend applications using Python and/or other backend language(s) such as Java, C#, JavaScript, Go, C/C++ - 2+ years of working with SQL and NoSQL databases - 2+ years working with common AWS or GCP services, or a similar platform - 2+ years of being a maintainer of a heavily used library or framework Nice to have - 2+ years of working with accelerator backed compute (GPU or TPU) - Experience with bazel as a build system At AssemblyAI, our goal is to attract and retain outstanding talent from diverse backgrounds, while ensuring fair pay among our team members. Our salary ranges are determined by competitive market rates that align with our company's size, stage, and industry. It's important to note that salary is just one aspect of the comprehensive compensation package we offer. When determining salaries, we consider various factors such as relevant experience, skill level, and qualifications evaluated during the interview process. We also strive to maintain internal equity by comparing salaries with those of peers on the team. While the salary range provided below serves as a general expectation for the posted position, we are open to considering candidates who possess more or less experience than specified in the job description. Should any updates arise regarding the expected salary range, we will communicate them accordingly. Please note that the provided range represents the anticipated base salary for candidates in the United States. For candidates outside of this region, there may be variations in the range, which we will communicate directly to applicants. Salary range: $140,000-$170,000 USD Our Team Our team is made up of problem solvers, innovators and top AI researchers with over 20+ years of experience in Machine Learning, NLP, and Speech Recognition from companies like DeepMind, Google Brain, Meta, Apple and Amazon. They conduct cutting edge deep learning research and develop novel algorithms & techniques to continually push the state of the art in speech recognition & NLP! Our team is fully remote, and our culture is super collaborative, low-ego, transparent, and fast-paced. We want to win - and have a flat organization where everyone can openly share ideas (regardless of their title or position) in order to get the best idea. As a remote company, our team members are given a lot of trust and autonomy to work where and how they want. We look for people to join our team who are ambitious, curious, and self-motivated, and we put a lot of trust and autonomy into everyone on our team. We want to empower everyone to do their best work with whatever tools, structures, or resources they need to perform at their highest potential. Benefits (US) - Competitive Salary + Bonus - Equity - 401k - 100% Remote team - Unlimited PTO - Premium Healthcare (100% Covered for you + dependents) - Vision & Dental Care - $1K budget for your home office setup - New Macbook Pro (or PC if you prefer) - 2x/year company paid team retreat APPLY ON THE COMPANY WEBSITE To get free remote job alerts, please join our telegram channel “Global Job Alerts” or follow us on Twitter for latest job updates. Disclaimer:  - This job opening is available on the respective company website as of 3rdJuly 2023. The job openings may get expired by the time you check the post. - Candidates are requested to study and verify all the job details before applying and contact the respective company representative in case they have any queries. - The owner of this site has provided all the available information regarding the location of the job i.e. work from anywhere, work from home, fully remote, remote, etc. However, if you would like to have any clarification regarding the location of the job or have any further queries or doubts; please contact the respective company representative. Viewers are advised to do full requisite enquiries regarding job location before applying for each job.   - Authentic companies never ask for payments for any job-related processes. Please carry out financial transactions (if any) at your own risk. - All the information and logos are taken from the respective company website. Read the full article

[aiwikiweb]

Tips and Tricks for Getting the Best Transcription Results with AssemblyAI

AssemblyAI offers a powerful AI-driven solution for audio transcription, but there are some best practices you can follow to ensure the highest accuracy. Here are some tips and tricks to help you make the most of AssemblyAI.

Tip 1: Use High-Quality Audio Files

Explanation: The accuracy of transcriptions depends on the quality of the audio. Use high-quality recordings with minimal background noise to ensure the best results.

Tip 2: Leverage Speaker Identification for Multi-Speaker Audio

Explanation: Enable the speaker identification feature to automatically label different speakers, making it easy to follow conversations in interviews, podcasts, or meetings.

Tip 3: Use Real-Time Transcription for Live Events

Explanation: Take advantage of AssemblyAI’s real-time streaming API for live events like webinars and meetings, allowing participants to access transcriptions in real-time.

Tip 4: Utilize Sentiment Analysis for Deeper Insights

Explanation: Use the sentiment analysis feature to understand the tone of conversations, providing valuable insights for interviews, customer feedback, or podcast episodes.

Tip 5: Edit and Proofread Transcripts

Explanation: While AssemblyAI offers high accuracy, it’s always a good idea to review and edit the transcriptions for any minor errors, especially in names or specialized terminology.

Use these tips to maximize the accuracy and utility of your transcriptions with AssemblyAI.

Visit aiwikiweb.com/product/assembly-ai/

[9kmovies-biz]

Remote Sales Engineer Job at AssemblyAI -Jobsclub

AssemblyAI is a remote-first AI company building powerful deep learning models for developers, startups, and enterprises to transcribe and understand their audio data. Our Automated Speech Recognition (ASR) models already outperform companies like Google, AWS, and Microsoft – which is why hundreds of companies and thousands of developers are using our APIs to transcribe and understand millions of…

[3acesnews]

AssemblyAI Enhances Universal Speech-to-Text Model for English, German, and Spanish