Compare the Top Speech Recognition Software that integrates with Vocode as of October 2025

This a list of Speech Recognition software that integrates with Vocode. Use the filters on the left to add additional filters for products that have integrations with Vocode. View the products that work with Vocode in the table below.

What is Speech Recognition Software for Vocode?

Speech recognition software uses artificial intelligence to interpret and recognize human speech. It is used in a variety of applications, such as transcription services, voice command systems, and automated customer service programs. The technology works by analyzing input sound waves and mapping them to a database of known words or phrases to generate an output. Compare and read user reviews of the best Speech Recognition software for Vocode currently available using the table below. This list is updated regularly.

  • 1
    Deepgram

    Deepgram

    Deepgram

    Deploy accurate speech recognition at scale while continuously improving model performance by labeling data and training from a single console. We deliver state-of-the-art speech recognition and understanding at scale. We do it by providing cutting-edge model training and data-labeling alongside flexible deployment options. Our platform recognizes multiple languages, accents, and words, dynamically tuning to the needs of your business with every training session. The fastest, most accurate, most reliable, most scalable speech transcription, with understanding — rebuilt just for enterprise. We’ve reinvented ASR with 100% deep learning that allows companies to continuously improve accuracy. Stop waiting for the big tech players to improve their software and forcing your developers to manually boost accuracy with keywords in every API call. Start training your speech model and reaping the benefits in weeks, not months or years.
    Starting Price: $0
  • 2
    Whisper

    Whisper

    OpenAI

    We’ve trained and are open-sourcing a neural net called Whisper that approaches human-level robustness and accuracy in English speech recognition. Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. We show that the use of such a large and diverse dataset leads to improved robustness to accents, background noise, and technical language. Moreover, it enables transcription in multiple languages, as well as translation from those languages into English. We are open-sourcing models and inference code to serve as a foundation for building useful applications and for further research on robust speech processing. The Whisper architecture is a simple end-to-end approach, implemented as an encoder-decoder Transformer. Input audio is split into 30-second chunks, converted into a log-Mel spectrogram, and then passed into an encoder.
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