2025 年頂級音訊轉錄工具背後的 AI 模型

Voice transcription tools are everywhere—from meetings and lectures to podcasts and interviews. But what powers these tools under the hood? Behind every accurate, real-time transcription app is a powerful Automatic Speech Recognition (ASR) model.

In this article, we break down the core speech-to-text models used by leading transcription tools like VOMO，Notta, Otter.ai, Fireflies, and more.

Why Does the Choice of Model Matter?

In general, the ASR (Automatic Speech Recognition) model determines most of a transcription tool's performance, including accuracy, transcription speed, multilingual support, and cost.

If the same model is used, the accuracy and speed of different audio-to-text tools will not vary significantly.

Accuracy (especially with accents or noise)

Speed (real-time vs batch)

Language support

Cost (API pricing or compute requirements. )

Cost has a significant impact on the pricing strategies of major transcription tools.

AI large models are expensive to run, so tools that are based on them typically offer little to no free trial.

In contrast, machine learning-based Otter provides a generous free plan, but the trade-off is lower accuracy.

For example:

• If you needmultilingual transcription, Whisper is hard to beat.
• Fordeveloper integration, Google and Deepgram offer flexible APIs.

The Core AI Models Behind Modern Transcription Tools

1. Whisper by OpenAI

Used by: VOMO, Notta, Trint (partially), Descript (in some workflows)

What it is

Whisper is a powerful open-source ASR model trained on 680,000 hours of multilingual and multitask supervised data collected from the web.

It has been out for over two years now, and few models have seriously challenged its dominance. However, its performance in languages other than English—such as Chinese—is still less than ideal.

Strengths:

Supports over 50 languages

Handles accents and noisy environments well

Offers translation and transcription in one step

Use case: Great for international transcription, long-form audio, and research.

2. Google Speech-to-Text API

Used by: Early versions of Otter, Notta (certain modes), Rev.ai (some workflows)

What it is

A commercial-grade ASR API from Google Cloud with support for 120+ languages and dialects.

If you see an audio transcription tool claiming to support 120 languages, you can be fairly certain it's most likely using Google's API.

Strengths:

Real-time and batch transcription

Word-level timestamps

Custom vocabulary and speaker diarization

Use case: Ideal for scalable business apps with high language flexibility.

3. Deepgram

Used by: Fireflies.ai, CallRail, Verbit

What it is: Deepgram uses end-to-end deep learning models trained specifically on call and meeting audio.

Strengths:

High accuracy in phone calls and meetings

Ultra-low latency

Models tuned by industry (finance, healthcare, etc.)

Use case: Ideal for sales calls, Zoom meetings, and call centers.

4. Amazon Transcribe

Used by: Temi, select SaaS platforms

What it is: AWS’s scalable ASR service supporting real-time and batch transcription.

Strengths:

Custom vocabulary

Language identification

Integrated with AWS ecosystem

Use case: Best for cloud-first enterprise workflows.

5. Microsoft Azure Speech Services

Used by: Enterprise tools and voice assistants

What it is: Microsoft’s robust speech API supporting transcription, translation, and speech synthesis.

Strengths:

Real-time transcription with punctuations

Speaker identification

Multilingual translation

Use case: Versatile, secure, and ideal for corporate tools.

6. Custom / Hybrid Models

Many top tools build on these models or combine them with proprietary enhancements.

🔹 Otter.ai

Now uses: Custom hybrid model (no longer depends on Google).

Otter used to rely heavily on Google’s machine learning models, which is one of the main reasons many users criticized it for its low transcription accuracy.

Optimized for: Meetings, with contextual awareness and speaker tracking

Bonus: Offers automatic summaries and slide capture

🔹 Notta

Uses: Whisper, Google STT, and others (depending on audio language and quality)

Bonus: Lets users choose between standard and “AI-enhanced” transcriptions

🔹 Fireflies.ai

Uses: Whisper, Deepgram, and internal models

Unique: Lets users switch between engines for best accuracy

ASR Model Comparison Table

ToolCore Model(s) UsedSupports WhisperProprietary ModelBest ForVOMOMicrosoft Azure + Whisper + Deepgram✅ Yes❌ NoFast and Accurate TranscriptionNottaWhisper + Google + hybrid✅ Yes❌ NoMultilingual audioOtter.aiCustom Hybrid (formerly Google)❌ No✅ Yes Meetings & summariesFireflies.aiDeepgram + Whisper + Custom✅ Yes✅ YesCall & meeting transcriptionsTrintWhisper (partially)✅ Yes❌ NoVideo editing + transcriptionRev.aiCustom + Google API (early)❌ No✅ YesHuman-level transcription

Final Thoughts

Choosing a transcription tool isn’t just about UI or features—it’s about the AI model powering the engine. Whether you're a student, journalist, or business professional, knowing what’s under the hood can help you pick the most accurate, efficient, and cost-effective solution for your needs.

If you're curious to test tools powered by different models, platforms like Notta and Fireflies.ai give you that flexibility.

Want to explore Whisper-powered tools?
Check out VOMO.ai, a fast and accurate transcription service powered by Whisper and designed for meetings, notes, and more.