Automatic Speech Recognition Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Automatic Speech Recognition
Vili Dhamo
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
- Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
- Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
- Orchestrated pipelines with Azure Data Factory
- Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
- Led the Data Engineering team (3 members) in a functional role
- Conducted workshops to optimize and stabilize the data platform and the development process
- Collected and prioritized new requests, maintained the product backlog
- Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural
Sara Ali
Last position:
Research Associate and Data Scientist at National Center of Robotics and Automation - Condition Monitoring Lab
- Developed ASR and TSR-based speech processing pipelines on AWS, enabling efficient feature extraction and scalable deployment for speech and text analytics.
- Built a Multimodal Speech Emotion Recognition system combining NLP and deep learning (audio + text), achieving 98% accuracy and supporting real-time, cloud-based inference.
- Designed and optimized end-to-end model training and evaluation workflows using AWS services (S3, EC2, Lambda) to ensure performance, reliability, and reproducibility.
- Created and deployed interactive, user-friendly dashboards for data visualization and insight generation, supporting research teams and management in data-driven decision-making.
Olaf Titel
Last position:
CTO, Partner, Agile Coach, Product Owner at fluidx digital GmbH
- Development and launch of a browser-based platform for camera streaming, augmented reality (AR), and visual computing
- Device-independent camera streaming for smartphones, tablets, desktop PCs, as well as VR and AR headsets
- Ensuring GDPR-compliant hosting in Open Telekom Cloud and other sovereign cloud providers
- Intuitive visual and collaborative features to boost efficiency and seamlessly integrate into existing enterprise software
Shivansh Dania
Last position:
Product Manager at Es Magico
- Launched an AI marketplace aggregator leveraging deep knowledge of e-commerce and marketplaces that reduced product listing time by 65% and improved compliance rates by 70% for a US based client
- Implemented AI-based content generation and image validation features based on marketplace-specific SEO principles and selling policies, eliminating up to 8 hours of manual work per week
- Created an intelligent inventory system with demand forecasting algorithms that decreased stockouts by 30% while optimizing cross-platform inventory allocation across multiple sales channels
- Engineered a conversational AI sales agent to achieve <600ms response latency and >92% intent classification accuracy, ensuring natural, human-like interactions across 10+ Indian languages
- Integrated persona-based conversation flows that improved engagement and upsell conversions by 28% using LLMs, SLMs, RAG and the latest STT and TTS technologies
- Delivered an enterprise-grade real estate CRM and lead management system that reduced lead management effort by 60% through automated workflows
- Architected role-based access controls after conducting 50+ stakeholder interviews to identify critical security needs
- Managed sprint execution with 90% on-time feature delivery while maintaining technical quality standards
- Developed a global VOIP application from concept to launch by defining product strategy based on competitive analysis for an Australian client
- Reduced onboarding friction by 60% through UX optimization, resulting in 25% higher user conversion rates
- Executed data-driven prioritization that accelerated time-to-market while balancing technical constraints
Josphat Githuka Muthoni
Last position:
Data Annotation Lead at Sigma AI
- Lead a team of 15 annotators on large-scale computer vision projects for autonomous vehicle systems
- Developed comprehensive annotation guidelines that improved inter-annotator agreement by 35 percent
- Implemented quality control processes that reduced error rates by 42% across all projects
- Collaborated with ML engineers to identify edge cases and improve dataset quality
- Managed annotation projects for Fortune 500 clients, delivering 100% on time
João Mira
Last position:
Founder & Builder at Franzie
- Led the "0 to 1" development and launch on App Store and Google Play, utilizing Mixpanel analytics to validate and refine features during a 100-user beta.
- Built the full-stack architecture using React Native and Supabase, integrating Gemini and ElevenLabs APIs to power personalized AI stories and speech-to-text.
- Engineered a custom spaced repetition algorithm to optimize vocabulary retention and personalize the learning path for users.
Ebrahim Wali
Last position:
Certified Trainer for Mach Software for the State of Berlin at HKR Senfin Berlin
- Fund management
- Budget
- Mach BI
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Statistics of experts using Automatic Speech Recognition
Aggregated from the professional profiles of matched freelancers.
Experience
19 years
Position duration
1.9 years (Germany: 2 years)
Positions per freelancer
8 (Germany: 13)
Top business areas
Information Technology, Project Management, Product Development
Top industries
Information Technology, Education, Automotive
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 90%)
Master's degree or higher
50% (Germany: 62%)
Doctorate
17% (Germany: 10%)
Certifications per freelancer
2
Most common languages
German, English, Spanish
Speak two or more languages
100% (Germany: 96%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Berlin using Automatic Speech Recognition
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Speech to text
Automatic Speech Recognition, often called ASR or speech-to-text, converts spoken audio into text. Companies use it for live captions, meeting notes, call transcripts, voice interfaces, search in audio archives, and quality checks on recorded conversations.
Where it fits
It shows up in products and operations that need fast text from voice:
- Customer support and contact center transcription
- Meeting, lecture, and media captioning
- Voice assistants and dictation tools
- Audio search, indexing, and compliance review
Common tooling
Strong specialists work across audio pipelines, model tuning, and evaluation. They know how to handle VAD, diarization, punctuation, timestamps, noisy audio, and custom vocabulary. They also understand when to use cloud speech APIs, open models, or a hybrid setup.
What good work looks like
Good output is readable, timed well, and stable across speakers, accents, and noisy rooms. It should preserve names, product terms, and domain phrases, while flagging uncertainty instead of guessing. For Berlin teams, this often matters in multilingual products, media workflows, and support operations.
When to bring in help
Bring in freelance expertise when a prototype must become production-grade, when transcription quality drops, or when you need faster setup than an internal team can deliver. Companies also bring in specialists for model comparison, prompt-free speech pipelines, error analysis, and integration into existing systems.
Skills that matter
Look for professionals who can work with audio formats, language models, APIs, and evaluation data. They should be comfortable with latency, streaming versus batch processing, privacy concerns, and domain adaptation. Clear communication matters too, because speech projects often involve product, legal, support, and content teams.
Frequently asked questions
Before you brief your next project: the most common questions about Automatic Speech Recognition.
Automatic Speech Recognition turns spoken audio into text that software can store, search, or process. Teams use it for captions, call transcripts, voice commands, and meeting summaries. It is also called ASR or speech-to-text, especially when discussing implementation details.
ASR is the technology behind transcription, but it can do more than create plain text. Good systems add timestamps, speaker separation, punctuation, and confidence signals. That makes them useful for search, analytics, and live experiences, not just document creation.
Bring in a speech recognition specialist when audio quality is inconsistent, domain terms keep failing, or the product needs streaming output instead of batch files. Freelancers are also useful when you need a quick evaluation of different APIs or open models. They can help move from proof of concept to a stable workflow.
A strong ASR specialist usually understands audio preprocessing, diarization, language modeling, API integration, and evaluation. In many projects, Python, cloud speech services, and data handling matter as much as the recognition model itself. For Berlin teams, multilingual handling can be important as well.
Automatic Speech Recognition is faster and easier to scale, but it can struggle with accents, noise, and specialized vocabulary. Human transcription is often better for legal or highly sensitive material, while ASR is a strong fit for operational speed and searchable text. Many teams use both in a review workflow.
Ask what audio they have worked with, how they handle noisy recordings, and how they measure output quality. A good speech-to-text specialist will also explain latency, privacy, and how domain terms are added or corrected. You want someone who can discuss trade-offs, not just API names.
Most Automatic Speech Recognition work can be done remotely because the core tasks are audio review, model setup, and integration. On-site sessions help when stakeholders need to align on product goals, compliance, or live testing with internal recordings. Many Berlin companies use a hybrid setup for that reason.
Look for clear testing on your own audio, not just generic demos. A strong ASR freelancer can explain where errors come from, how they reduce them, and when the system should defer instead of guessing. Good work is measurable, stable, and easy for your team to maintain.
The average hourly rate of freelancers in Berlin, Germany who have used Automatic Speech Recognition in their recent projects is 91 €, which corresponds to a daily rate of about 730 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Automatic Speech Recognition in their recent projects, 100% hold at least a Bachelor's degree, 50% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Automatic Speech Recognition in their recent projects have 19 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Berlin, Germany who have used Automatic Speech Recognition in their recent projects are German (100%), English (100%), and Spanish (14%).
The most common industries among freelancers in Berlin, Germany who have used Automatic Speech Recognition in their recent projects are Information Technology (100%), Education (57%), and Automotive (43%).
The most common business areas among freelancers in Berlin, Germany who have used Automatic Speech Recognition in their recent projects are Information Technology (100%), Project Management (86%), and Product Development (71%).
Main locations of FRATCH Experts, who have recently used Automatic Speech Recognition
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
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Philipp Thomaschewski
FRATCH CEO
