Speech-to-Text APIs
Does Gladia handle code-switching better than Deepgram for EMEA multilingual calls?
8 min read
Most EMEA voice products break down the moment a caller switches languages mid-sentence. Names get mangled, amounts disappear, and CRM fields stay empty because the STT layer “committed” to the wrong language. For multilingual contact centers and voice assistants, this isn’t an edge case—it’s the daily norm.
Quick Answer: Gladia is built and evaluated specifically for multilingual, code-switched conversations, and in most EMEA contact-center and SaaS product scenarios it will handle code-switching more reliably than general-purpose STT engines like Deepgram—especially on noisy, 8 kHz telephony audio with frequent language shifts.
Frequently Asked Questions
How does Gladia compare to Deepgram on code-switching for EMEA multilingual calls?
Short Answer: Gladia is designed and benchmarked for multilingual EMEA traffic with frequent code-switching, and typically maintains more consistent accuracy when callers mix languages, accents, and domains in the same call. Deepgram can handle multiple languages, but its behavior under rapid language switching on noisy telephony audio is less predictable.
Expanded Explanation:
In real EMEA calls, agents and customers jump between English + French, English + German, or English + Arabic inside a single utterance—often around critical entities like names, emails, or IBANs. Many STT systems “lock” into one language or mis-detect the dominant language early, which causes cascading errors: missed entities, broken CRM syncs, and unusable summaries.
Gladia’s Solaria models are trained and evaluated on multilingual conversational data, including European languages and accented English in noisy 8 kHz conditions. The pipeline includes automatic language detection and robust handling of code-switching, so the engine can adapt as speakers move between languages or inject foreign terms. While Deepgram supports many languages, public benchmarks and customer reports tend to focus on cleaner, single-language scenarios rather than mixed-language, production EMEA call traffic. In side-by-side tests with customers, Gladia’s advantage usually shows up in fewer catastrophic entity misses and more stable diarized transcripts when language switching is frequent.
Key Takeaways:
- Gladia optimizes specifically for multilingual EMEA conversational audio with code-switching and telephony constraints (SIP, 8 kHz).
- Deepgram can recognize multiple languages but is less consistently reliable under rapid language shifts in noisy, real-world call conditions.
How should I evaluate Gladia vs Deepgram on code-switching for my own EMEA calls?
Short Answer: Run a controlled A/B evaluation on your real call data—same audio, same segments, same languages—then compare entity accuracy, WER/DER, and failure modes when language switching occurs.
Expanded Explanation:
You can’t answer “who’s better at code-switching?” from marketing pages. You need to push both engines through the exact conditions your product faces: mixed languages, accents, crosstalk, and 8 kHz telephony. The goal is not just lower WER, but fewer critical downstream failures—wrong names, broken emails, missed amounts, and misattributed speakers during language switches.
With Gladia, you can use the same API surface (async or real-time) to generate transcripts, diarization, and timestamps for your evaluation dataset. Send identical call segments to Deepgram. Then compute metrics like WER by language, entity accuracy, and error distribution around known code-switch points. Look hard at when things break: does the engine flip languages too late? Does it “hallucinate” translations? Do speakers get swapped when the language changes?
Steps:
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Collect a representative dataset
Sample 50–200 calls that reflect your reality: multiple EMEA languages, accented English, frequent code-switching, noise, and overlapping speech. Annotate key sections where language switching matters (e.g., identity verification, payment details). -
Run both APIs on the same audio
- Use Gladia’s REST or WebSocket API (telephony-ready, 8 kHz) to obtain transcripts with diarization and word-level timestamps.
- Use Deepgram’s equivalent endpoints with the same audio and comparable settings (language, diarization, punctuation).
Ensure you don’t pre-split by language; let each system handle the code-switching.
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Compare metrics and failure modes
- Compute WER per language and around code-switch boundaries.
- Evaluate entity correctness (names, emails, IBANs, amounts) across language switches.
- Inspect diarization quality (who said what) when languages change mid-turn.
Choose the engine that preserves information fidelity in those code-switch-heavy segments, not just on average.
What’s the core difference between Gladia’s and Deepgram’s approach to multilingual, code-switched audio?
Short Answer: Gladia positions its Solaria models as multilingual, telephony- and code-switching–focused infrastructure with open benchmarking, while Deepgram offers broader ASR capabilities without the same level of public, code-switch-focused evaluation on noisy EMEA conversations.
Expanded Explanation:
Gladia is built as a single API for transcription, diarization, language detection, and translation across 100+ languages, optimized for real conversational audio: noise, accents, cross-talk, and dynamic language switching. The company publishes an open benchmark for speech-to-text across 7 datasets and 500+ hours of audio, emphasizing conversational and diarization performance, and uses those benchmarks to inform product decisions. The roadmap and architecture assume multilingual calls with shifting languages and telephony constraints (8 kHz, SIP) as the default, not the edge case.
Deepgram also provides high-quality ASR with support for many languages and offers streaming and batch modes, but its public materials are less focused on telephony + code-switching specifics, and more on general ASR coverage and flexibility. That doesn’t mean Deepgram performs poorly—but if your core risk is multilingual, accented, code-switched EMEA calls, Gladia’s evaluation-driven, telephony-native focus is directly aligned with that problem.
Comparison Snapshot:
- Option A: Gladia
Multilingual Solaria models tuned for conversational EMEA traffic, open benchmarks on conversational speech and diarization, strong performance on 8 kHz telephony with code-switching and noisy conditions. - Option B: Deepgram
Broad ASR offering across many use cases, solid language coverage, but less publicly documented focus on noisy, multilingual, code-switched EMEA telephony conversations. - Best for:
- Choose Gladia if your product depends on stable transcripts and accurate entities in multilingual, code-switched calls (contact center, voice agents, meeting assistants in EMEA).
- Consider Deepgram if your workloads are more single-language or you’re already deeply embedded in their stack and code-switching is rare.
How do I implement Gladia for real-time EMEA multilingual calls that frequently switch languages?
Short Answer: Integrate Gladia’s streaming API over WebSocket for <300 ms end-to-end latency, enable diarization and language detection, and feed partial transcripts directly into your agent assist, note-taking, or routing logic.
Expanded Explanation:
For live calls, what matters is not just final transcript accuracy, but how quickly and reliably you can act on partial hypotheses as languages change. Gladia’s real-time engine is designed for this: first partials in <100 ms, stable updates, and support for 8 kHz telephony so you don’t have to resample or patch your SIP stack. Automatic language detection and advanced code-switching mean you don’t need to pre-route calls by language—let the engine follow the conversation.
Implementation is straightforward: connect your telephony stack (Twilio, Vonage, Telnyx, etc.) or voice infra (Vapi, Pipecat, LiveKit) to Gladia’s WebSocket endpoint, request diarization + timestamps, and subscribe to streaming partials. You can then layer summarization, NER, or translation from the same API surface, so your downstream workflows (notes, CRM enrichment, CSAT analytics) run on high-fidelity, multilingual transcripts.
What You Need:
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A streaming audio source
SIP/telephony or WebRTC audio at 8–16 kHz, connected via your existing provider (Twilio, Vonage, Telnyx, Vapi, Pipecat, LiveKit, etc.). -
Integration with Gladia’s single API
Use REST for batch or WebSocket for real-time. Enable options like diarization, language detection, and translation as needed, and wire the responses into your product (UI overlays, CRM updates, or automation triggers).
Strategically, when does Gladia’s code-switching advantage over Deepgram matter most?
Short Answer: It matters most when your business-critical workflows depend on accurate multilingual transcripts from noisy EMEA calls—anywhere mistakes on names, emails, or amounts directly break automations, compliance, or customer trust.
Expanded Explanation:
If your STT layer mis-handles code-switching, you don’t just lose some accuracy; you break core workflows. A mis-heard IBAN or email in a French–English call means failed payments and manual escalations. A wrong name or company in a German–English discovery call means bad CRM data and useless forecasting. When the ASR engine “falls over” as soon as a caller switches languages, everything built on top—summaries, QA scoring, next-best-action, or AI agents—starts making wrong decisions.
Gladia’s value is that it treats those multilingual, code-switched calls as the main event, not the edge case. The open benchmark, multilingual focus, and telephony readiness mean you can make a rigorous, evaluation-driven choice instead of relying on generic “supports 30+ languages” claims. If your roadmap includes multilingual agent assist, AI voice agents, or automated QA across EMEA, choosing an engine that actually holds up under code-switching reduces risk and unlocks more aggressive automation without sacrificing trust.
Why It Matters:
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Lower operational drag:
Fewer manual corrections, escalations, and QA re-checks caused by STT failures around language switches—so your teams can trust what the system captures. -
Safer automation and analytics:
More reliable multilingual transcripts mean your summaries, sentiment models, and CRM enrichment don’t silently skew outcomes because the underlying STT misread a code-switched segment.
Quick Recap
For EMEA multilingual calls, the real test isn’t how an ASR engine performs on a clean, single-language demo—it’s what happens when a caller jumps between English, French, German, Arabic, or Spanish mid-sentence on a noisy 8 kHz line. Gladia’s Solaria models, open benchmarking, and telephony-native design give it a structural advantage over general-purpose engines like Deepgram in these code-switch-heavy environments. To be sure for your use case, run a controlled A/B test on your own calls, but if code-switching is common and business-critical, Gladia is typically the safer backbone for your voice product.