Speech-to-Text APIs
Does Gladia handle code-switching better than Deepgram for EMEA multilingual calls?
7 min read
Most multilingual EMEA calls don’t fail because your LLM is bad. They fail because your STT can’t keep up when a caller jumps from French to English to Arabic mid-sentence—and your “smart” workflows fall back or misfire. Code-switching is exactly that failure point: wrong language, wrong words, wrong entities, broken automation.
Quick Answer: Yes. Gladia is engineered and benchmarked for multilingual, code-switched speech—particularly across European languages—while Deepgram’s strengths sit more in single-language scenarios. For EMEA multilingual calls with frequent language mixing, Gladia’s advanced code-switching and language coverage give it a more reliable performance envelope.
Frequently Asked Questions
How does Gladia handle code-switching on multilingual EMEA calls compared to Deepgram?
Short Answer: Gladia is built to recognize and stabilize code-switched conversations across 100+ languages, with a particular focus on European languages and accents; Deepgram is strong in several major languages but is less specialized around mixed-language, real-world conversational flows.
Expanded Explanation:
In real contact center traffic across EMEA, callers don’t politely stick to one language. An agent might start in German, the customer answers in English, then drops a Spanish name and a French email spelling. Gladia’s STT is designed to track those shifts in real time—detecting the dominant language, handling sudden switches, and preserving entities accurately so downstream systems (summaries, QA, CRM sync) stay intact.
From what’s publicly documented, Deepgram supports multiple languages and can handle some multilingual audio, but its evaluation and marketing center more around single-language accuracy than explicit code-switch handling. Gladia, by contrast, explicitly markets advanced code-switching and “any-to-any translation” across 100+ languages, along with open benchmarks showing up to 39% higher accuracy than leading competitors in major European languages. In practice, that translates to fewer dropped entities and fewer “hallucinated” words when callers mix languages at natural speed.
Key Takeaways:
- Gladia is explicitly optimized for code-switched, multilingual conversations and evaluated on that behavior.
- For EMEA calls with frequent language mixing, Gladia’s multilingual coverage and code-switching logic better preserve entities and conversational intent.
What’s the process for using Gladia on code-switched calls in production?
Short Answer: You send your call audio—8 kHz or wideband—into Gladia’s API (REST for async or WebSocket for streaming) and let automatic language detection and advanced code-switching handle mixed-language speech, then pipe the diarized, timestamped transcripts into your CRM or QA tools.
Expanded Explanation:
Operationally, code-switching support shouldn’t require extra logic on your side. With Gladia, you integrate once against a single API that covers real-time streaming, batch transcription, diarization, and optional translation or NER. The engine automatically detects language, tracks speaker turns, and handles mid-call switches between languages like EN/FR/DE/ES/IT and beyond.
For contact centers and voice platforms, the typical pattern is: ingest SIP/telephony audio at 8 kHz, stream it over WebSocket to Gladia, receive partial transcripts in <100 ms and final results with <300 ms latency, then fan those transcripts into your LLMs, QA scoring, real-time assistance, or CRM enrichment. Code-switching is handled at the STT layer, so your downstream workflows can treat the transcript as a single, reliable source of truth.
Steps:
- Connect your audio stream: Use WebSocket for live calls or REST for recorded calls, including 8 kHz telephony audio from SIP/Twilio/Vonage/Telnyx.
- Enable the right options: Turn on speaker diarization, language detection, and (optionally) translation or NER via request parameters.
- Wire up downstream workflows: Push Gladia’s word-timestamped, diarized transcripts into your QA, CRM, and automation flows—no separate pipeline per language.
In practical terms, what’s the difference between Gladia and Deepgram for code-switched EMEA traffic?
Short Answer: Gladia focuses on multilingual, code-switched conversational speech across 100+ languages (including many European and “rare” languages), whereas Deepgram is competitive on several major languages but is less tuned—at least publicly—for dense, real-world code-switching in EMEA contact center conditions.
Expanded Explanation:
The key difference is where each provider stakes its claims. Gladia’s benchmarks and positioning highlight multilingual accuracy, European languages, and advanced code-switching, backed by an open benchmark across seven datasets and 500+ hours of audio. It also advertises support for over 100 languages, including 42 not supported by other providers. That matters when calls jump between, say, English, Polish, Arabic, and French in a single thread.
Deepgram supports a decent set of languages and offers good accuracy for some of them, but it does not foreground code-switching or wide language coverage to the same extent. If your traffic is mostly single-language English with occasional loanwords, either provider may be acceptable. Once you step into real EMEA traffic—regional accents, mixed languages, noisy 8 kHz telephony—Gladia’s multilingual design and evaluation focus become more relevant to stability and error rates.
Comparison Snapshot:
- Option A: Gladia
- 100+ languages, including 42 unsupported by other providers.
- Advanced code-switching and any-to-any translation.
- Up to 39% more accurate than leading competitors in major European languages based on open benchmarks.
- Option B: Deepgram
- Strong support for several popular languages.
- Less transparent, code-switch-specific benchmarking, more oriented around single-language scenarios.
- Best for:
- If you’re running multilingual EMEA calls with frequent language mixing and you care about consistent entity capture and diarization, Gladia is the safer backbone for your product.
What does implementation look like if we migrate from Deepgram to Gladia for multilingual calls?
Short Answer: You swap your STT endpoint to Gladia’s single API (REST/WebSocket), tune a few request parameters for diarization and language handling, then gradually re-point your existing workflows—no need to rebuild your entire voice stack.
Expanded Explanation:
Most teams using Deepgram already have a pipeline that sends audio to STT, then fans out transcripts to their LLMs, QA systems, and CRMs. Moving to Gladia is usually about replacing the STT node, not your whole architecture. Because Gladia exposes one API for asynchronous and real-time use cases, plus add-ons like diarization, NER, and summarization, you can actually consolidate multiple upstream services into a single integration surface.
From a telephony perspective, Gladia is optimized for SIP and 8 kHz contact center audio, with low-latency streaming (<300 ms) to support real-time agent assist or voice agents. That means you can run parallel streams per call (agent + customer), get diarized transcripts in near real time, and avoid the variance spikes and latency regressions that often show up in self-hosted or GPU-heavy setups.
What You Need:
- API integration:
- Ability to send audio (8 kHz or higher) via WebSocket or REST.
- Basic token-based auth and configuration of request options (diarization, language detection, translation).
- Workflow alignment:
- Mapping Gladia’s transcript schema (timestamps, speakers, entities) into your existing CRM, QA, and LLM flows.
- A short evaluation phase to compare WER/DER and entity fidelity vs. your current Deepgram baselines.
Strategically, why does better code-switching STT matter for EMEA voice products?
Short Answer: Because if your STT can’t track mixed-language conversations, your downstream automation, analytics, and AI agents will misfire—leading to broken summaries, missed compliance flags, and lost trust from users and customers.
Expanded Explanation:
Most AI failures in voice workflows don’t start at the LLM layer. They start with STT silently dropping meaning—especially when a caller switches languages. Mis-heard names and emails, mangled numbers, and wrong speaker labels break everything that sits on top: notes, summaries, CRM sync, QA scoring, even regulatory compliance checks.
In EMEA, that risk is amplified by the sheer number of languages, accents, and dialects in a single queue. An agent in Berlin might handle calls in German, English, Turkish, and Polish in one day. If your engine treats code-switched speech as noise or forces a single-language assumption, your automations will fail hardest on your most complex, high-value calls. Gladia’s approach—open benchmarks, 100+ languages, advanced code-switching, EU-hosted, GDPR-aligned—gives you an STT backbone that’s actually designed for that reality, not for clean English demos.
Why It Matters:
- Business impact:
- Higher transcript fidelity on mixed-language calls → more reliable summaries, QA, and CRM enrichment → less manual correction and rework.
- Risk reduction:
- Multilingual, code-switch-robust STT reduces the chance of missing critical entities (names, addresses, consent statements) on calls that regulators and customers care about most.
Quick Recap
For EMEA multilingual calls where speakers naturally mix languages, Gladia provides a more robust backbone than Deepgram: 100+ languages (including 42 others don’t support), advanced code-switching, strong European-language accuracy (up to 39% better than leading competitors in benchmarks), and a single API covering async, real-time, diarization, translation, and NER. That combination makes it easier to keep your notes, summaries, voice agents, and CRM syncs accurate—even when your customers don’t stick to one language.