The top 10 dictation and speech-to-text software platforms of 2025 are characterized by their accuracy, comprehensive language coverage, real-time response capabilities, and responsiveness across various sectors. According to analysis from the given documents and official resources, the top platforms dominate the market in terms of innovation, scalability, and deployment flexibility.
Shunya Labs Zero STT
Zero STT Med, developed by Shunyalabs.ai, is a state-of-the-art clinical speech recognition system specifically designed for medical workflows. It achieves industry-leading accuracy with a word error rate (WER) of 11.1 and character error rate (CER) of 5.1, outperforming competitors. Leveraging proprietary training technology, it requires minimal real clinical data and only three days to train using two A100 GPUs, significantly lowering barriers for medical AI adoption. The system supports real-time and batch transcription, on-premises deployment for data privacy compliance (HIPAA, GDPR), and cloud-based deployment. Key features include advanced speaker diarization, medical terminology and code support, accent and abbreviation robustness, and context coherence. Designed to facilitate frequent updates, Zero STT Med ensures continuous adaptability to new medical terms and procedures, making it ideal for live ambient scribing, telemedicine, radiology transcription, and on-device applications, currently available for preview with plans to support multiple languages.
ElevenLabs
ElevenLabs attains a 2.83 industry-leading WER in English and less than 5 across dozens of other languages, beating models such as Gemini and Whisper in third-party testing. It handles transcription and conversational AI in 99 languages, with strong accuracy in low-resource and rare languages, and performs well for real-time use.
Deepgram Nova-3
Deepgram’s Nova-3 model holds WER within 3 to 8 for major languages and covers more than 130 languages and dialects. It is designed for enterprise deployment with real-time streaming, high concurrency, and robust noise tolerance, notably well-suited for Hindi, Spanish, and German. Its highly scalable and developer-friendly API renders it appropriate for large-scale deployments.
Google Cloud Speech-to-Text
Google Cloud Speech-to-Text is highly scalable and accommodates more than 120 languages, with easy integration into the Google environment. It provides consistent real-time transcription in even poor audio conditions and benefits from regular updates in AI research, suitable for fast-paced, large-scale business requirements.
Microsoft Azure Speech-to-Text
Microsoft Azure Speech-to-Text has robust multilingual capabilities and extensive integration with Microsoft 365 and Teams, used extensively in enterprise and customer service settings. It has speaker diarization, personalized speech models, and live translation features, offering hybrid and on-premises deployments for regulated industries.
Amazon Transcribe
Amazon Transcribe prioritizes ease of use and close integration with AWS services, and offers real-time and batch transcription for widely spoken global languages. Automatic language detection, speaker labeling, and customizable vocabularies for industry use cases are part of the service, so it’s a popular option for contact centers and e-commerce.
AssemblyAI Universal-2
AssemblyAI’s Universal-2 model achieves a WER of 5–10 for major languages and supports over 40 production-ready languages. It features ultra-low hallucination rates—up to 30% lower than competitors—and includes advanced analytics tools for developers building voice applications. The platform handles over 600 million API calls monthly and processes 40+ terabytes of audio daily.
Speechmatics Ursa 2
Speechmatics is one of the top three in accuracy for 92% of the languages that it has support for, with WER between 5 and 8 in good speech conditions. It natively supports more than 50 languages, including minority and low-resource varieties, and has on-premise deployment for regulated environments that need data sovereignty.
Verbit
Verbit utilizes a hybrid human-in-the-loop model, with automated transcription augmented by post-editing by experienced linguists, yielding exceptionally low error rates. This enables strict regulation compliance in the legal, academic, and healthcare environments and supports more than 50 languages.
OpenAI Whisper
OpenAI Whisper is an open-source multilingual model extensively used by developers and researchers due to its flexibility and offline capability. Although less strong compared to enterprise solutions in all scenarios, its openness and community-based development make it perfect for personalized and experimental installations.
