Speech-to-Text APIs
Gladia vs Deepgram for SIP/8kHz audio — which one is more accurate on phone calls?
7 min read
Most phone-call transcription issues don’t come from your LLM or routing logic. They start at the very bottom: bad SIP/8 kHz audio handling. If your STT can’t reliably decode compressed, narrowband telephony audio, everything downstream—notes, QA, CRM sync, automations—quietly breaks.
Quick Answer: For SIP/8 kHz phone calls, Gladia is generally more accurate and more stable than Deepgram, especially on noisy, multilingual contact center audio where diarization and entity capture actually matter.
Frequently Asked Questions
Which is more accurate on phone calls: Gladia or Deepgram?
Short Answer: Based on open benchmarks for conversational speech and diarization, Gladia delivers lower error rates than Deepgram, which translates into fewer missed words, cleaner speakers, and more reliable transcripts on SIP/8 kHz calls.
Expanded Explanation:
On real-world conversational speech, Gladia’s Solaria models achieve up to 45% lower word error rate (WER) than competing APIs. Deepgram v3 sits significantly higher in that benchmark set. For diarization, Gladia also shows up to 3× lower diarization error rate (DER) than alternatives, including Deepgram, across meetings, broadcast, field recordings, and other noisy scenarios.
If you’re running telephony-heavy workloads—contact centers, IVR flows, or voice assistants over SIP—those deltas matter more than they look on paper. Lower WER/DER means fewer broken customer names, cleaner email/number capture, and fewer “ghost speakers” in your transcripts. That’s the difference between a CRM update that just works and one that quietly corrupts data.
Key Takeaways:
- Gladia’s open benchmarks show lower WER on conversational speech and lower DER for diarization than Deepgram.
- On phone calls, that gap shows up as better entity capture, more robust speaker separation, and fewer downstream failures.
How do I evaluate Gladia vs Deepgram on SIP/8 kHz audio for my own use case?
Short Answer: Run a controlled A/B evaluation on your own call audio: same files, same segments, same metrics (WER and DER), and compare performance end-to-end on the workflows you care about.
Expanded Explanation:
Public benchmarks are a useful anchor, but contact center reality is messy: codecs, background noise, accents, and overlapping speech vary a lot. The only credible way to choose between Gladia and Deepgram for SIP/8 kHz is to run a reproducible bake-off with your own calls.
For telephony, you should test on real recordings pulled from production: think mixed-language customer support calls, high-stress complaint calls, and sales discovery sessions with lots of interruptions. Instrument the evaluation to measure not just WER/DER, but also functional accuracy on entities (names, emails, numbers, ticket IDs) and impact on downstream workflows (how many CRM updates fail, how many summaries are unusable).
Steps:
- Curate your test set: 50–200 representative calls, including noisy, accented, and multilingual segments, all at SIP/8 kHz.
- Run both APIs: Send the exact same audio to Gladia and Deepgram using their batch or streaming endpoints; store raw transcripts and diarization outputs.
- Score and inspect: Compute WER/DER on a labeled subset, then manually review critical errors (entities, speaker mix-ups, hallucinated content) to see which provider actually breaks fewer workflows.
How does Gladia compare to Deepgram in handling real-world call conditions (noise, accents, crosstalk)?
Short Answer: Gladia is optimized for real-world conversational audio—including accents, code-switching, crosstalk, and background noise—whereas Deepgram’s performance tends to regress more under these constraints in comparative benchmarks.
Expanded Explanation:
Most STT demos use clean, studio-quality audio. Contact centers don’t. You get VoIP artifacts, airpods in noisy kitchens, overlapping agents and customers, and callers switching languages mid-sentence. This is exactly the regime where Gladia’s models are designed to hold up, and where its benchmark advantage shows.
In the open conversational speech benchmark, built from real meetings, customer calls, and voice agents, Gladia maintains significantly lower WER than Deepgram v3. On diarization benchmarks, Gladia’s DER is up to 3× lower across environments like courtrooms, restaurants, and field recordings—all of which share characteristics with challenging call center audio: background chatter, reverberation, overlapping speakers. That robustness is what keeps your notes, summaries, and QA analytics from silently degrading as soon as conditions get messy.
Comparison Snapshot:
- Gladia: Trained and evaluated on noisy conversational speech; lower WER and DER on real-world audio; strong on accents, code-switching, and crosstalk.
- Deepgram: Competitive on cleaner audio; comparatively higher error rates reported in open benchmarks on challenging conversational data.
- Best for: If your primary workload is SIP/8 kHz phone calls with multilingual users and non-ideal audio, Gladia is better suited to preserve information fidelity.
How do I implement Gladia for SIP/8 kHz phone call transcription?
Short Answer: You connect your telephony stack (e.g., Twilio, Vonage, Telnyx, or your SIP infrastructure) to Gladia’s single API—via REST for async or WebSocket for streaming—and send 8 kHz audio directly for real-time or batch transcription, diarization, and add-ons.
Expanded Explanation:
Gladia is designed to drop into existing voice infrastructure without a pile of glue code. For SIP/8 kHz, you typically have two options:
- Real-time streaming: Capture the RTP audio from your SIP trunk or your CPaaS provider and stream it via WebSocket to Gladia’s real-time endpoint. You’ll get partial transcripts in <100 ms and stable final results with sub-300 ms latency—enough for natural, turn-by-turn IVR or live agent-assist.
- Async/batch: For post-call analytics, recordings, and compliance workflows, push your call recordings (WAV, MP3, etc.) over REST to the batch endpoint and retrieve complete transcripts with word-level timestamps, diarization, and optional NER, sentiment, or summarization.
Because Gladia exposes all of this through a single API surface, you don’t have to juggle separate services for transcription, diarization, and analysis; you wire it in once and then layer use cases on top.
What You Need:
- Telephony integration point: Access to your SIP/8 kHz audio stream or call recordings (via Twilio/Vonage/Telnyx, LiveKit/Vapi/Pipecat, or your own SBC/softswitch).
- API integration: A small service that opens a WebSocket/REST connection to Gladia, forwards audio, and consumes transcripts plus metadata for your downstream systems (CRM, ticketing, QA, BI).
Strategically, why choose Gladia over Deepgram for a phone-call heavy product?
Short Answer: If your product’s success depends on trustworthy call data—accurate entities, clean speakers, and stable latency—Gladia’s accuracy benchmarks, telephony focus, and data controls make it a safer backbone than Deepgram for long-term SIP/8 kHz workloads.
Expanded Explanation:
In phone-call products, “good enough” STT is rarely good enough. Small differences in WER and DER compound across millions of calls into broken automations, bad coaching data, and mistrust in your platform. Gladia’s differentiator is not just raw model performance, but how that translates into predictable, auditable behavior in production.
On the quality side, Gladia openly publishes benchmarks across 7 datasets and 500+ hours of audio, including conversational speech and diarization, with an open-sourced methodology. That transparency gives you a concrete baseline instead of vendor marketing. Operationally, Gladia is built to handle voice infrastructure realities: SIP/8 kHz optimization, multilingual support with 100+ languages, sub-300 ms real-time latency, and stable performance across infinite parallel streams.
On the trust side, Gladia treats security and privacy as defaults, not extras: GDPR, HIPAA, SOC 2, and ISO 27001 compliance, plus a clear policy of not using your audio to retrain models. For teams building regulated or enterprise-grade call products, that posture matters as much as WER.
Why It Matters:
- Higher-fidelity data → better automation: Fewer transcription and diarization errors mean more reliable summaries, better intent detection, and CRM enrichment that doesn’t poison your records.
- Predictable infrastructure → lower risk: Telephony-ready performance, stable latency, and strong compliance reduce the operational surprises that usually emerge only after you’ve scaled a call product.
Quick Recap
For SIP/8 kHz phone calls, the real question isn’t “Which demo sounds nicer?” but “Which engine preserves more information under contact-center conditions?” Gladia’s open benchmarks show lower WER on conversational speech and lower DER on diarization than Deepgram v3, and that advantage holds where it counts: noisy, accented, crosstalk-heavy calls. With a single API for real-time and batch, telephony-aware design, and strong security guarantees, Gladia is a strong default choice if your product’s reputation depends on transcripts you can actually trust.