![]() ![]() ![]() Although almost every vendor claims they offer “the best” and “the most accurate” Speech-to-Text software, the accuracy depends on the data. The best approach to evaluate Speech-to-Text software is to test it with real users and datasets in a real environment. For example, a Speech-to-Text model without customization can transcribe “arthritis” as “off right his.” Out-of-the-box Speech-to-Text engines mostly struggle with industry-specific jargon, special names or homophones. It shows the percentage of errors in the transcript performed by an Automatic Speech Recognition software compared to the human transcription with no mistakes.Ĭustomization can boost accuracy even further. WER is the ratio of edit distance between words in a reference transcript and the words in the output of the Speech-to-Text engine to the number of words in the reference transcript. Word Error Rate (WER) is the most commonly used method to measure the accuracy of Automatic Speech Recognition software. We noted a need for fact-based and transparent tools to enable data-driven decision-making. Going back to the title of this article, “the best” speech-to-text is the one that responds to your needs. A Speech-to-Text engine that works very well for one company may not be a fit for another one. The variety of use cases requires enterprises to evaluate Speech-to-Text (STT) solutions in line with their needs: accuracy, features, support, documentation, reliability, privacy & security, volume and cost. Even notorious voice assistants such as Alexa and Siri use Speech-to-Text engines following a wake word engine. Speech-to-Text offers tremendous benefits to enterprises for several use cases: transcription, dictation (voice typing), closed caption, subtitles or building analytics solutions (keyword detection, topic detection, auto summarization, content moderation, sentiment analysis, etc.). However, now with the availability, choosing the best voice recognition solution has also become more difficult, especially for the Speech-to-Text (STT) solutions. Recent advances in deep learning have made voice technology more accurate, accessible, and affordable. ![]()
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