List of IBM Watson Speech to Text Customers
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Since 2010, our global team of researchers has been studying IBM Watson Speech to Text customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased IBM Watson Speech to Text for Speech Recognition AI from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using IBM Watson Speech to Text for Speech Recognition AI include: Thurgood Marshall College Fund, a United States based Education organisation with 200 employees and revenues of $98.0 million, Vadion, a Pakistan based Professional Services organisation with 100 employees and revenues of $10.0 million, Lingmo International, a Australia based Professional Services organisation with 20 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using IBM Watson Speech to Text, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the Artificial Intelligence software purchases.
The IBM Watson Speech to Text customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of Artificial Intelligence software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Lingmo International | Professional Services | 20 | $2M | Australia | IBM | IBM Watson Speech to Text | Speech Recognition AI | 2016 | x |
In 2016, Lingmo International implemented IBM Watson Speech to Text to adopt a cognitive approach to speech recognition and accelerate model training cycles. The deployment used IBM Watson Speech to Text within the Speech Recognition AI category to enhance transcription accuracy and support rapid iterative training even with relatively small volumes of data.
Architecturally the implementation leverages Watson cloud APIs and SDK-driven pipelines to run both real time transcription and batch model training workflows, with configuration focused on acoustic model adaptation and language model customization appropriate for its product use cases. Functional capabilities emphasized include automated speech-to-text transcription, model retraining pipelines, and data ingestion and annotation processes to reduce time to iterate on models.
Operational scope centered on speech recognition and model training functions within the organization, enabling engineering and applied research workflows to iterate models faster. Governance centered on structured model versioning and iterative training cycles, and the outcome explicitly reported was much faster model training even with relatively small volumes of data.
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Thurgood Marshall College Fund | Education | 200 | $98M | United States | IBM | IBM Watson Speech to Text | Speech Recognition AI | 2017 | n/a |
In 2017 Thurgood Marshall College Fund implemented IBM Watson Speech to Text within a purpose built iOS application. The integration uses IBM Watson Speech to Text, classified as Speech Recognition AI, to convert recorded speech into machine readable text for subsequent processing and storage.
As the sole developer on a team of four non technical members the engineer built the app to capture audio, invoke the IBM Watson Speech to Text API for on demand transcription, and persist both the transcription and the original audio for later use. Functional modules implemented include audio capture, transcription orchestration, and data persistence for speech and text artifacts. The IBM Watson Speech to Text service was called directly from the mobile client to perform speech to text operations while the app retains the recorded audio file alongside the transcribed text.
Operational scope remained at the application level for Thurgood Marshall College Fund and the project was delivered within a small team context. The deployment centered on iOS client side orchestration with the IBM Watson Speech to Text application programming interface as the transcription engine, supporting content capture, archival, and text generation workflows for educational and administrative use. Governance and rollout were managed by the project team with development and storage decisions embedded in the mobile application architecture.
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Vadion | Professional Services | 100 | $10M | Pakistan | IBM | IBM Watson Speech to Text | Speech Recognition AI | 2018 | n/a |
In 2018, Vadion implemented IBM Watson Speech to Text to support its Speech Recognition AI initiatives within machine learning and NLP project work. Vadion is a Pakistan professional services firm of roughly 100 employees, and the IBM Watson Speech to Text deployment was explicitly used to underpin transcription and language modeling efforts across its data science and product engineering teams.
The implementation included a custom language model for IBM Watson Speech to Text, and a transcription post processing pipeline that incorporated an improved version of the Norvig spell corrector developed by Vadion engineers. Complementary capabilities included a TensorFlow based speech commands classifier built to recognize YES and NO utterances, and an address understanding system that applied NLP techniques to parse textual address data into structured fields. These modules were organized as language model adaptation, transcription normalization, and downstream NLP parsing components aligned to Speech Recognition AI functional workflows.
Architecturally the solution combined IBM Watson Speech to Text as the ASR core with in‑house TensorFlow models and NLP pipelines, and it coexisted with experiments using Google Cloud Natural Language for complementary language analysis. Audio input flowed into IBM Watson Speech to Text with the custom language model applied during decoding, followed by transcription post processing that fed TensorFlow classifiers and the address understanding pipeline. The integration pattern emphasized a hybrid transcription plus model inference workflow connecting speech recognition, model adaptation, and NLP extraction capabilities.
Governance focused on iterative model training and language model tuning, with versioned language models and scripted retraining cycles to refine transcription accuracy for domain specific vocabulary. Operational ownership rested with Vadion’s machine learning and engineering teams, who embedded the IBM Watson Speech to Text based Speech Recognition AI stack into client projects and internal NLP initiatives.
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Buyer Intent: Companies Evaluating IBM Watson Speech to Text
- Serverbike, a United Kingdom based Professional Services organization with 10 Employees
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