![]() Each result is hyperlinked to a diff of the human transcript versus each API's automatically generated transcript. In the table that follows, we outline the accuracy score that each transcription API achieved on each audio file. Finally, we compare the API's transcription with our human transcription to calculate Word Error Rate (WER) - more below.Second, we transcribe the files in our dataset by human transcriptionists-to approximately 100% accuracy.First, we transcribe the files in our dataset automatically through the specified APIs (AssemblyAI, Google, and AWS).This included audio taken from product demos, tutorial videos, documentaries, podcasts, sports talk radio, and corporate earnings calls. We used audio with a wide range of accents, audio quality, number of speakers, and industry-specific vocabularies. We also share results for the same audio using AssemblyAI's AI models such as Topic Detection, Keyword Detection, PII Redaction, Content Safety Detection, Sentiment Analysis, Summarization, and Entity Detection. Then we will present a side-by-side comparison of which ASR models-AssemblyAI, Google Cloud Speech-to-Text, and AWS Transcribe-have the highest transcription accuracy. Here, we will review audio files from a wide range of sources. Whether you are adding captions to your video content, reviewing phone calls to improve team member performance, or adding content safety and topic labels to a podcast, transcription accuracy is always a top priority. ![]() The use cases for Speech-to-Text transcription are almost limitless, but many of the most popular use cases combine Automated Speech Recognition (ASR) with meetings, phone calls, or media. In this benchmark report, we compare our latest v8 model architecture transcription accuracy between AssemblyAI, Google Cloud Speech-to-Text, and AWS Transcribe on a variety of audio use cases. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |