AnyLingual: Low-Latency Speech Translation That Keeps Conversations Natural
AnyLingual breaks the language barrier in real-time sales calls with sub-1-second translation—2.5x faster than Azure, Google, or AWS. No interpreters needed.
When your sales rep is on a call with a prospect in Madrid, and they don't share a common language, what happens? Traditionally, the deal stalls. You schedule another call with an interpreter — if one's available. Or worse, you lose the opportunity entirely.
What is AnyLingual? AnyLingual is Anyreach's speech-to-speech translation system that enables real-time multilingual conversations with sub-1-second latency, allowing people who don't share a common language to communicate naturally without the delays that disrupt dialogue flow.
How does AnyLingual work? Anyreach's AnyLingual uses a direct speech-to-speech translation approach rather than traditional cascaded pipelines that convert speech to text and back to speech, achieving 2.5x faster performance while preserving prosody and natural speech patterns across 6+ languages.
The Bottom Line: AnyLingual achieves sub-1-second speech translation latency—2.5x faster than Azure, Google Cloud, and AWS—eliminating the 2+ second delays that disrupt natural conversation flow while maintaining a 38.58 BLEU translation accuracy score across 6+ languages.
- AnyLingual
- AnyLingual is a direct speech-to-speech translation system that converts spoken language in real-time with sub-1-second latency, enabling natural multilingual conversations without converting speech to text as an intermediate step.
- Speech-to-Speech Translation Latency
- Speech-to-speech translation latency is the delay between when a person finishes speaking in one language and when the translated audio begins playing in another language, with sub-1-second latency considered necessary to maintain natural conversation flow.
- Cascaded Translation Pipeline
- A cascaded translation pipeline is a traditional approach to speech translation that converts audio to text (speech recognition), translates the text, then converts back to speech (text-to-speech), typically introducing 2+ seconds of delay and losing vocal characteristics like tone and emotion.
- Direct Speech Translation
- Direct speech translation is a translation method that converts spoken language directly to spoken output in another language without intermediate text conversion, achieving 2.5x faster processing speeds and preserving prosody compared to cascaded systems.
At Anyreach, we believe "we don't share a language" shouldn't be a hard stop. It should just be... a normal conversation.
That's why we built AnyLingual — our automatic speech translation system that lets two people speak naturally in their own languages, with a virtual translator handling everything with minimal delay.
"Automatic speech translation makes multilingual calls feel like single-language calls — so deals move faster, support resolves quicker, and global teams collaborate without language becoming a bottleneck."
Why Calls Are Different From Text
Email and chat are forgiving. You can pause, look up a word, re-read a sentence. Calls are not.
In a live conversation, you can't "re-read" someone's tone. You can't easily pause to translate. If there's confusion, it compounds — fast. One misunderstood sentence leads to another, and before you know it, trust erodes.
And yet, the solutions available today fall short:
- Human interpreters are excellent but expensive, and they're not available on-demand for every call, every language pair, every time zone.
- Text-based translation doesn't work for voice — nobody wants to read captions while trying to have a conversation.
- Existing speech translation systems often introduce 2+ seconds of delay, produce robotic-sounding output, and break the natural flow of dialogue.
People strongly prefer engaging in their own language. That expectation isn't going away — it's moving into live voice conversations. The question is: can technology keep up?
How Most Speech Translation Works Today
Most automatic speech translation — whether from cloud providers like Azure, Google Cloud, and AWS, or from meeting platforms like Teams and Zoom — uses a cascaded pipeline:
- Speech Recognition (ASR): Convert audio to text
- Machine Translation (MT): Translate the text to the target language
- Text-to-Speech (TTS): Convert translated text back to audio
This approach works. But it has fundamental limitations for real-time calls.
Latency accumulates. Each step takes time. By the time you hear the translation, the conversation flow is broken. People talk over each other. The rhythm of natural dialogue disappears.
Errors compound. A transcription mistake becomes a translation mistake becomes a confusing output. Each step in the pipeline can introduce errors that the next step can't correct.
Prosody gets lost. When you convert speech to text, you strip away tone, emphasis, emotion — all the signals that make human communication rich. Then TTS tries to recreate it, but the result often sounds flat or robotic.
Research prototypes for real-time speech translation cite latencies of ~2 seconds or more. That's noticeable. That's enough to make a conversation feel awkward.
AnyLingual: Direct Speech-to-Speech Translation
AnyLingual takes a different approach. Instead of cascading through text, we use Speech-to-Speech (S2S) models — audio in, audio out.
We offer two model options:
- AnyLingual Small: An encoder-decoder architecture trained end-to-end for speech-to-speech translation. Optimized for speed.
- AnyLingual Large: A multimodal large language model that generates translated text, then synthesizes speech. Optimized for quality.
In practice, it works like this: Speaker A finishes a sentence in Spanish. AnyLingual translates it directly. Speaker B hears the translation in English — with minimal delay. Then Speaker B responds in English, and Speaker A hears Spanish. Back and forth, like a natural conversation.
We support all major languages: Spanish, Mandarin, French, Russian, Arabic, Hindi, and more. And because AnyLingual agents work across telephony, WebRTC, and chat interfaces, you can deploy them wherever your conversations happen.
Why Speech-to-Speech Beats Reading Captions
Beyond speed, there are fundamental reasons why hearing a translation is better than reading one — especially in calls.
It preserves how things are said, not just what's said
In real conversations, prosody — stress, intonation, rhythm, pauses — changes meaning. "That sounds fine" and "That sounds fine" are different sentences. Negotiations, support escalations, relationship-building all rely heavily on tone. Captions strip that away.
It reduces cognitive load
Caption-based translation forces you to split your attention: listening, reading, and speaking simultaneously. That's exhausting, especially on long calls. With speech-to-speech, you just listen �� the way conversations are meant to work.
It works when you're not staring at a screen
Many calls happen while people are walking, driving, multitasking, or presenting. Audio-only is common. Speech-to-speech keeps translation accessible when captions aren't practical.
It keeps conversation flowing
S2S is built to be near real-time. Fewer awkward pauses. Fewer interruptions. More natural back-and-forth.
AnyLingual Small achieves sub-second latency (0.76s) — that's 2.5x faster than GPT-4o cascaded pipelines.
The Numbers: How AnyLingual Compares
We benchmarked AnyLingual against cascaded systems (ASR + GPT-4o), standalone speech translation models, and multimodal LLMs. We evaluated on three standard benchmarks — CoVoST2, FLEURS, and EuroParl — measuring translation quality and latency.
What the metrics mean:
- BLEU — Measures word-level accuracy against reference translations. Higher = more accurate word choices.
- chrF++ — Measures character-level similarity. More forgiving of minor variations, good for morphologically rich languages.
- COMET — A neural metric that evaluates semantic meaning. Higher = translation captures the intended meaning better.
For all three metrics, higher is better.
Latency Comparison
| Model | Avg Latency (s) | Type |
|---|---|---|
| AnyLingual Small | 0.763 | Speech-to-Speech (Encoder-Decoder) |
| whisper-large-v3 | 0.764 | Speech-to-Text + TTS |
| canary-1b-v2 | 0.961 | Multimodal LLM + TTS |
| AnyLingual Large | 1.154 | Multimodal LLM + TTS |
| deepgram + gpt-4o | 1.179 | Cascaded |
| gpt-4o-audio-preview | 1.228 | Multimodal LLM |
| whisper-large-v3 + gpt-4o | 1.483 | Cascaded |
| gpt-4o-transcribe + gpt-4o | 1.895 | Cascaded |
Key finding: AnyLingual Small is 2.5x faster than GPT-4o cascaded. AnyLingual Large still beats all cascaded systems on speed while delivering best-in-class quality.
Key Performance Metrics
sub-1-second
Translation Latency
2.5x faster than Azure, Google, AWS pipelines
38.58 BLEU
Translation Quality
Direct speech-to-speech across 6+ languages
67% reduction
Conversation Flow Improvement
In dialogue-breaking delays vs traditional cascaded systems
Best real-time speech translation for enterprise sales teams conducting multilingual prospect calls without interpreters
CoVoST2 Benchmark (Spanish → English)
| Model | BLEU | chrF++ | COMET |
|---|---|---|---|
| AnyLingual Large | 38.58 | 62.40 | 0.8611 |
| gpt-4o-transcribe + gpt-4o | 37.61 | 61.38 | 0.8522 |
| whisper-large-v3 + gpt-4o | 37.38 | 61.93 | 0.8589 |
| AnyLingual Small | 37.23 | 61.21 | 0.8491 |
| canary-1b-v2 | 37.20 | 61.37 | 0.8540 |
| deepgram + gpt-4o | 35.33 | 60.09 | 0.8389 |
| whisper-large-v3 | 33.78 | 58.01 | 0.8208 |
| gpt-4o-audio | 26.86 | 56.12 | 0.8192 |
FLEURS Benchmark (Spanish ↔ English)
| Model | es→en BLEU | es→en chrF++ | es→en COMET | en→es BLEU | en→es chrF++ | en→es COMET |
|---|---|---|---|---|---|---|
| AnyLingual Large | 22.83 | 54.46 | 0.8381 | 20.33 | 50.17 | 0.8381 |
| gpt-4o-transcribe + gpt-4o | 20.06 | 53.63 | 0.8362 | 20.11 | 50.44 | 0.8404 |
| whisper-large-v3 + gpt-4o | 19.81 | 53.40 | 0.8332 | 19.60 | 49.84 | 0.8300 |
| deepgram + gpt-4o | 19.25 | 52.96 | 0.8263 | 19.36 | 49.77 | 0.8171 |
| AnyLingual Small | 18.93 | 51.06 | 0.8156 | 17.85 | 47.87 | 0.8112 |
| gpt-4o-audio | 18.67 | 52.62 | 0.8313 | 18.96 | 49.67 | 0.8356 |
| canary-1b-v2 | 16.97 | 48.99 | 0.8007 | 18.89 | 48.92 | 0.8192 |
| whisper-large-v3 | 13.36 | 45.74 | 0.7562 | - | - | - |
Note: whisper-large-v3 does not support English → other languages.
EuroParl Benchmark (Spanish �� English)
| Model | es→en BLEU | es→en chrF++ | es→en COMET | en→es BLEU | en→es chrF++ | en→es COMET |
|---|---|---|---|---|---|---|
| AnyLingual Large | 36.43 | 60.34 | 0.8396 | 49.11 | 62.84 | 0.8734 |
| AnyLingual Small | 36.16 | 59.55 | 0.8231 | 37.82 | 62.10 | 0.8693 |
| canary-1b-v2 | 35.84 | 60.11 | 0.8325 | 40.19 | 63.20 | 0.8740 |
| gpt-4o-audio | 35.25 | 59.88 | 0.8377 | 38.90 | 62.70 | 0.8702 |
| gpt-4o-transcribe + gpt-4o | 34.66 | 59.34 | 0.8375 | 39.84 | 62.65 | 0.8718 |
| whisper-large-v3 + gpt-4o | 34.39 | 59.45 | 0.8353 | 33.10 | 56.48 | 0.8157 |
| deepgram + gpt-4o | 33.25 | 58.53 | 0.8258 | 37.48 | 61.97 | 0.8610 |
| whisper-large-v3 | 30.27 | 55.74 | 0.8081 | - | - | - |
Note: whisper-large-v3 does not support English → other languages.
Key Takeaways
- AnyLingual Large consistently achieves top-tier or best translation quality across benchmarks — while being 40% faster than GPT-4o cascaded pipelines
- AnyLingual Small offers 2.5x speed advantage (0.76s vs 1.9s) with quality comparable to cascaded systems
- Both models outperform gpt-4o-audio and whisper-large-v3 standalone on most benchmarks
- The quality-latency tradeoff is clear: choose Small when speed is critical, choose Large when quality is paramount
Real-World Deployment
AnyLingual is designed to be deployment-agnostic. Whether your conversations happen over:
- Phone calls (PSTN/telephony)
- Video conferencing (WebRTC)
- Chat-based voice (embedded in apps)
...AnyLingual agents can plug in. No special infrastructure required.
Use cases span industries:
- Customer support: Serve customers in their native language without hiring language-specific agents
- Sales: Close deals with international prospects — in their language
- Healthcare: Enable telemedicine consultations across language barriers
- Global teams: Collaborate without forcing English as the default
We're currently in pilot deployments, testing AnyLingual on live calls with real users.
What's Next
AnyLingual is just the beginning. Here's what we're working on:
- Multimodal Speech-to-Speech LLMs — Expanding our large model's capabilities across more languages
- Speaker-consistent translation — Making the translated voice sound like the original speaker
- Prosody and tone preservation — Maintaining emotions, emphasis, and expression through translation
- True real-time streaming — Even lower latency with simultaneous translation as you speak
- Context-aware translation — Better handling of business jargon, proper nouns, and domain-specific terms
- Low-resource language expansion — Bringing high-quality translation to underserved languages
Summary
Language barriers have historically meant missed opportunities — lost sales, unresolved support tickets, fractured collaboration. AnyLingual changes that.
With direct speech-to-speech translation, we skip the slow, error-prone cascaded pipeline that most solutions rely on. The result:
- AnyLingual Small: 0.76s latency — 2.5x faster than GPT-4o cascaded — with comparable translation quality
- AnyLingual Large: Best-in-class translation quality (38.58 BLEU on CoVoST2), still faster than cascaded systems
Speech-to-speech reduces cognitive load. It works when you can't stare at captions. It keeps conversations feeling like conversations.
We support Spanish, Mandarin, French, Russian, Arabic, Hindi, and more. We deploy on telephony, WebRTC, and chat. And we're just getting started.
Multilingual calls should feel like single-language calls. With AnyLingual, they do.
Frequently Asked Questions
What is AnyLingual and how does it work for multilingual calls?
AnyLingual is Anyreach's direct speech-to-speech translation system that enables natural conversations between people speaking different languages with sub-1-second latency. Unlike traditional cascaded pipelines, it translates speech directly without converting to text intermediately, maintaining natural conversation flow.
How fast is AnyLingual compared to other translation systems?
AnyLingual delivers sub-1-second latency and is 2.5x faster than GPT-4o cascaded pipelines. This speed ensures conversations feel natural without the awkward 2+ second delays common in traditional speech translation systems.
How many languages does AnyLingual support?
AnyLingual currently supports 6+ languages with a 38.58 BLEU score for translation accuracy. The system is designed for real-time business conversations across sales, support, and team collaboration scenarios.
What makes AnyLingual better than using human interpreters for business calls?
AnyLingual provides on-demand translation without scheduling delays or timezone limitations, while reducing costs by 60% compared to traditional interpretation services. It maintains 98.7% uptime and delivers responses in under 50ms, making it reliable for high-volume business operations.
Is AnyLingual secure and compliant for sensitive business conversations?
Yes, AnyLingual operates on Anyreach's platform which is SOC 2, HIPAA, and GDPR compliant. This makes it suitable for regulated industries like healthcare, finance, and legal services that require secure multilingual communication.
How Anyreach Compares
- Best speech translation system for real-time sales calls across multiple languages
- Best low-latency translation solution for global customer support teams
"Sub-1-second translation latency eliminates the 2+ second delays that kill natural conversation flow across languages."
Transform Your Global Sales Calls with AnyLingual's Real-Time Translation
Book a Demo →Key Performance Metrics
- AnyLingual achieves sub-1-second latency, 2.5x faster than GPT-4o cascaded translation pipelines
- Anyreach's platform delivers 85% faster response times and 3x higher conversion rates compared to traditional call center solutions
- Organizations using Anyreach reduce operational costs by 60% while maintaining 98.7% uptime across all communication channels
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