Why Shunya is a stronger choice than Speechmatics for production speech-to-text

Speechmatics is a good enterprise speech platform. It has built its reputation around speech recognition in difficult real-world conditions, including accents, dialects, noisy audio, multiple speakers, real-time transcription, and multilingual speech. Its current platform supports 56+ languages, real-time and batch transcription, speaker diarization, custom dictionaries, translation, summaries, and cloud, on-premise, and on-device deployment.

Its latest Melia 1 model is also a major step forward, with automatic language detection and code-switching across 56+ supported languages. Speechmatics has published recent evaluations showing strong results on Arabic, Mandarin, and Tamil code-switching.

That makes Speechmatics a strong option.

But Shunya’s advantage is not simply that it supports more languages.

The stronger case is that Shunya combines specialized speech models, production-scale inference, speech intelligence, Indian-language depth, code-switching, domain-specific recognition, and flexible deployment into one speech stack.

Shunya Zero STT currently publishes 3.10% composite WER across eight OpenASR benchmarks, 216+ languages, and streaming performance under 500ms first-token. Its model family includes dedicated models for general speech, Indian languages, code-switched speech, and healthcare.

More importantly, Shunya’s output does not have to stop at the transcript. Its production speech stack can move from audio to transcript to intent, sentiment, emotion, and speaker information, making ASR part of a broader voice intelligence workflow.

For enterprises choosing an ASR platform, that changes the decision.

The question isn’t simply:

Which service transcribes audio better?

It is:

Which speech platform gives us the best combination of accuracy, latency, model specialization, scale, deployment, and intelligence for the conversations our business actually handles?

The production ASR problem is bigger than transcription

The difference between a good transcription demo and a production speech system becomes obvious as soon as real users start talking.

Customers don’t speak in clean benchmark conditions.

They call from noisy rooms.

They use cheap headsets.

They speak quickly.

They interrupt each other.

They use abbreviations.

They pronounce names differently.

They switch languages in the middle of a sentence.

And they mention products, organizations, medicines, account numbers, and technical terms that a generic model may not expect.

Speechmatics explicitly builds around this problem. Its current product positioning emphasizes real-world audio, accents, dialects, background noise, overlapping speech, diarization, and alphanumeric accuracy.

Shunya takes a similar production-first position, with Zero STT explicitly designed around noise, accents, phone-line audio, multiple speakers, code-switching, and names that may not exist in standard English dictionaries.

The important differentiation is what happens after you identify that the audio is difficult.

Do you keep tuning one general model?

Or do you route the workload to a model that was built around that specific speech problem?

That’s where Shunya’s architecture becomes particularly interesting.

Shunya’s biggest advantage is model specialization

Shunya’s current documentation describes a model family rather than a single ASR endpoint.

Zero STT Universal

For broad general-purpose speech across international languages.

Zero STT Indic

Purpose-built for Indian languages and regional dialects, including lower-resource Indian speech.

Zero STT Codeswitch

For conversations where speakers naturally move between languages, including Hinglish and Tanglish.

Zero STT Med

For clinical conversations, medical terminology, drug names, procedures, ICD codes, and healthcare workflows.

This approach matters because not every speech problem is fundamentally the same.

A model optimized for broad multilingual speech has one job.

A model optimized for Indian-language speech has another.

A model handling code-switched speech has another.

A clinical model has another.

Shunya lets enterprises choose the model according to the workload.

Speechmatics also has multiple models. Its current platform includes Enhanced, Standard, and Melia 1, with Melia focused specifically on multilingual code-switching.

The distinction is therefore not simply “multiple models versus one model.”

It is that Shunya makes domain and speech-environment specialization a central part of its production ASR architecture.

Accuracy should be measured where the business risk is

A lower Word Error Rate (WER) is useful.

But it isn’t sufficient.

Shunya currently publishes a 3.10% composite WER in English across eight OpenASR benchmarks, covering datasets including LibriSpeech, SPGISpeech, TED-LIUM, AMI, VoxPopuli, GigaSpeech, and Earnings22.

The same benchmark reports 146.23× real-time throughput, making inference efficiency part of the published performance picture.

Shunya also publishes 11.9% average Hindi WER across seven datasets, giving enterprises a specific metric for a language that is often important in Indian deployments.

Speechmatics has an established accuracy story as well. Its product emphasizes accent and dialect recognition in noisy audio, and its public material includes strong results across languages and real-world speech.

That is why buyers should avoid choosing between the two purely from a leaderboard.

For an enterprise system, ask:

How many customer names are wrong?

How many account numbers are wrong?

How many product codes are wrong?

How many medical terms are wrong?

How often does the transcript fail when speakers overlap?

What happens when the speaker changes language?

A 0.5% improvement in general WER is less valuable than eliminating the specific mistakes that break your application.

Alphanumeric accuracy is a surprisingly important differentiator

Speech systems often look excellent until users start saying things such as:

“Account number 438729.”

“Reference ID ABX-2047.”

“EMI is ₹24,500.”

“Order SKU K8-19.”

These are not ordinary language-recognition problems.

They are precision problems.

Speechmatics explicitly highlights alphanumeric recognition and currently publishes 96.9% sequence accuracy on character strings, 98.0% on digits, and 85.4% on mixed alphanumeric strings.

That’s an important capability for financial services, telecom, logistics, customer support, and other transaction-heavy environments.

Shunya’s production positioning similarly calls out numbers, domain vocabulary, names, and phone-line speech as important real-world conditions.

This is precisely the kind of capability that should be tested using real customer recordings rather than generic benchmark audio.

If your application regularly captures account numbers or reference IDs, measure character-level accuracy, not just WER.

Indian speech needs more than a language dropdown

Speechmatics supports Indian languages, including Hindi, Bengali, Marathi, Tamil, and others.

Its broader multilingual strategy is a genuine strength.

Shunya takes a more specialized approach to India.

Its Zero STT Indic model covers 55+ Indian languages and 40+ dialects, including lower-resource languages such as Bhojpuri, Magahi, Maithili, and Chhattisgarhi.

The distinction is important.

Indian speech recognition is not simply:

Hindi → English

or:

Tamil → text

The same customer interaction may contain:

Hindi + English + brand names + regional pronunciation + numbers + informal speech.

Shunya’s product architecture treats this combination as a core workload.

That’s why there is a separate Indic model, and a separate code-switching model, rather than relying only on one broad multilingual model.

For an Indian bank, insurer, telecom company, healthcare provider, or government service, this can be a much more important differentiator than the number of languages listed on a website.

Code-switching is now a serious benchmark category

This is an area where Speechmatics has become particularly strong.

Its Melia 1 model supports automatic language detection, language labelling, and code-switching across 56+ supported languages.

Speechmatics’ September 2026 testing reported that Melia 1 had the lowest mixed error rate among the tested models on Arabic-English and led the evaluated systems on Mandarin and Tamil code-switching. It also reported improvements in alphanumeric string handling.

So Shunya’s code-switching story needs to be specific.

Shunya’s Zero STT Codeswitch is built around mixed Indic-English speech, with native handling of code-switched patterns such as Hinglish and Tanglish.

This creates two different strengths.

Speechmatics: broad multilingual code-switching across its supported global languages.

Shunya: dedicated specialization around Indic code-switching and Indian conversational speech.

For an Indian contact center, the second can be highly relevant.

For a global multilingual media platform, Speechmatics’ broader Melia approach may be more attractive.

The correct choice comes down to which code-switching your users actually produce.

Context and terminology: dictionaries are useful, but models matter

Speechmatics provides a Custom Dictionary for brand names, technical terminology, jargon, acronyms, and other specialized vocabulary. The current product allows up to 1,000 domain-specific terms.

This is useful for enterprises.

Shunya also supports keyterm normalization, while specialized models address domains where terminology is deeply intertwined with the speech itself.

There is an important practical difference between:

“Recognize this term.”

and

“Understand speech in which this entire vocabulary is normal.”

For example, a medical transcription workflow may contain:

drug names

dosages

procedures

anatomical terminology

ICD codes

abbreviations

specialized clinician language

Shunya’s Zero STT Med is purpose-built around these healthcare requirements.

Speechmatics also has a dedicated Medical Model, so healthcare should be treated as a head-to-head benchmarking category rather than an automatic Shunya win.

The right metric is critical medical terminology accuracy, not general WER.

Real-time performance is a system property

Both platforms support real-time speech recognition.

Speechmatics describes its real-time API as low-latency transcription with partial results arriving within a few hundred milliseconds, alongside diarization and other real-time features.

Shunya currently publishes under-500ms streaming performance for Zero STT.

For a voice application, however, the key metric isn’t simply “supports streaming.”

You need to understand:

Time to first token

How quickly does the first usable text arrive?

Finalization latency

How quickly does the system settle on the correct words?

Stability

How much does the transcript change as more audio arrives?

Throughput

How quickly can the infrastructure process audio?

Concurrency

How many simultaneous sessions can you sustain?

Shunya currently publishes 146× real-time throughput and 240+ concurrent streams per GPU.

Speechmatics’ current Pro plan supports 50 concurrent real-time sessions, while Enterprise provides higher scale and no rate limits.

These aren’t directly equivalent metrics, because one describes published GPU concurrency and the other describes a service-tier limit.

But they highlight a key production question:

What happens when your call volume goes from 50 conversations to 500 or 5,000?

That’s when inference efficiency becomes an architectural concern.

High concurrency can change the economics of ASR

For a small application, transcription cost is often thought of as:

minutes × price per minute.

At enterprise scale, that becomes much more complicated.

A contact center might have thousands of simultaneous sessions.

A voice-AI provider might process multiple calls for multiple customers.

A media platform might need high-volume batch transcription.

At that point, throughput and concurrency become major cost drivers.

Shunya publishes 240+ concurrent streams per GPU, giving engineering teams a concrete performance metric to evaluate.

Speechmatics abstracts infrastructure through its managed service and offers higher concurrency through Enterprise, with its current Pro tier supporting 50 concurrent real-time sessions.

For managed deployments, this can make Speechmatics easier to operate.

For teams optimizing high-volume inference economics, Shunya’s published per-GPU concurrency and throughputprovide a different architecture to evaluate.

The important thing is to benchmark cost per concurrent conversation, not just cost per minute.

Deployment flexibility is important for enterprise AI

Speechmatics is particularly strong on deployment flexibility.

Its current platform supports cloud, on-premise, and on-device deployments. It also describes edge and hybrid deployment options for organizations that need low latency or local data processing.

Shunya also positions itself around cloud, on-premise, edge, and air-gapped environments, with documentation describing CPU-compatible inference in supported configurations.

That makes both platforms substantially more flexible than cloud-only transcription APIs.

For an enterprise evaluation, deployment should therefore include:

Data residency

Audio retention

Network dependency

Hardware requirements

CPU versus GPU requirements

Air-gapped support

Upgrade process

Operational ownership

Shunya’s CPU-compatible positioning can be especially interesting for organizations that want private inference without standing up a dedicated GPU infrastructure layer.

Speech-to-text becomes much more valuable when it produces intelligence

This is where Shunya’s broader platform creates another important differentiator.

For most businesses, transcription isn’t the final objective.

They want to know:

Why did the customer call?

Was the customer frustrated?

What did they ask for?

Did the agent resolve the issue?

What should happen next?

Shunya’s Zero STT experience explicitly moves from:

Audio → Transcript → Intent → Sentiment → Emotion → Speaker Labels.

That turns ASR into a speech intelligence layer rather than simply a transcription service.

The same idea extends into Shunya’s broader platform, which combines STT, TTS, voice agents, small language models, edge speech understanding, and knowledge-grounded voice AI.

Speechmatics also offers capabilities beyond transcription, including translation, summaries, chapters, sentiment, topics, and diarization.

So the distinction isn’t that one platform has post-transcription intelligence and the other doesn’t.

The difference is that Shunya’s broader architecture is designed around speech as the input layer for downstream voice intelligence and voice-AI systems.

For companies building call intelligence, voice agents, conversational systems, and speech-driven workflows, that can reduce the number of disconnected services required in the stack.

Speechmatics is strong at speaker separation

Speaker diarization is another area where both platforms are serious.

Speechmatics supports speaker and channel diarization, including overlapping and messy conversations, and provides speaker-aware real-time transcription.

Shunya also exposes speaker labels and diarization as part of its intelligence pipeline.

But speaker separation should be tested with your actual environment.

A clean two-person interview is easy.

A customer-service call with:

customer + agent + supervisor

plus interruptions and background speech is much harder.

For contact centers, evaluate:

Speaker assignment accuracy

Overlap handling

Turn detection

Timestamp alignment

Behavior during interruptions

These can matter more than a generic diarization checkbox.

Pricing: the cheapest minute is not always the cheapest system

Speechmatics currently has a strong public pricing position.

Its July 2026 pricing lists Melia 1 batch from $0.129/hour, Standard batch at $0.24/hour, Enhanced batch at $0.40/hour, and additional real-time tiers starting at $0.24/hour. The Pro tier includes 50 concurrent real-time sessions, while Enterprise provides unlimited scale, custom models, and higher concurrency.

Shunya currently lists:

Zero STT: $0.0039/minute

Zero STT Indic: $0.0045/minute

Zero STT Codeswitch: $0.0050/minute

Zero STT Med: $0.0050/minute

Its capability documentation also lists these model-level prices.

That puts standard Zero STT at approximately $0.234/hour before volume discounts.

So there is no credible reason to say:

Shunya is simply cheaper than Speechmatics.

For some batch transcription workloads, Speechmatics is cheaper on public list pricing.

The more important question is cost for the required outcome.

If you need an Indic-specific model, compare the Indic workload.

If you need code-switching, compare the relevant code-switching models.

If you need speech intelligence, include downstream service costs.

If you need high concurrency, include infrastructure economics.

If you need private deployment, include the operational cost of running the solution.

Price per minute is only the starting point.

Where Speechmatics is the better choice

Speechmatics is an excellent choice when your priorities are:

Global multilingual ASR

Its platform supports 56+ languages, with Melia 1 designed for multilingual and code-switched audio.

Accents and dialects

Speechmatics has built its technology around robust recognition across diverse accents, dialects, and noisy audio.

Real-time transcription

It has a mature low-latency streaming stack with diarization and other real-time capabilities.

Global code-switching

Melia 1 provides native multilingual code-switching across 56+ languages.

Flexible deployment

Cloud, on-premise, on-device, and hybrid options are all part of the current Speechmatics platform.

Competitive batch pricing

Melia 1 currently starts at $0.129/hour for batch transcription.

For organizations with these requirements, Speechmatics is a very strong platform.

Where Shunya makes a stronger case

Shunya’s strongest positioning is around production specialization.

Choose Shunya when:

Indian speech is core to the product

The ASR stack includes a dedicated 55+ language Indic model rather than treating Indian speech as a small subset of a general multilingual model.

Code-switching is a normal part of the interaction

Zero STT Codeswitch is specifically built for mixed Indic-English speech.

Different domains require different models

Universal, Indic, Codeswitch, and Med models allow the speech layer to be aligned with the workload.

High concurrency matters

Shunya publishes 240+ concurrent streams per GPU and 146× real-time throughput.

The transcript is not the final output

Shunya can take speech into intent, sentiment, emotion, diarization, and speaker information within its speech intelligence workflow.

You are building a larger voice-AI system

Shunya’s wider platform connects STT, TTS, voice agents, SLMs, edge speech understanding, and knowledge-grounded intelligence.

You need private or resource-efficient deployment

Shunya supports enterprise deployment options including on-premise, air-gapped, and CPU-compatible configurations.

Shunya vs Speechmatics by workload

Workload	Stronger fit
Broad global multilingual transcription	Speechmatics / Both
Accent and dialect-heavy audio	Both
Indian-language contact centers	Shunya
Hinglish / Indic code-switching	Shunya
Global multilingual code-switching	Speechmatics Melia 1
Medical transcription	Both
Real-time transcription	Both
Very high-concurrency inference	Shunya
Low-cost batch transcription	Speechmatics
Speech + intent / sentiment / emotion	Shunya
Speaker diarization	Both
On-premise deployment	Both
On-device deployment	Both
CPU-oriented private inference	Shunya
Broader voice-AI stack	Shunya
Multilingual speech platform with mature deployment options	Speechmatics

How to benchmark Shunya and Speechmatics

A serious enterprise evaluation should begin with your own recordings, not a generic test file.

Build a representative sample containing:

Clean speech

Phone-quality audio

Background noise

Multiple speakers

Overlapping speech

Regional accents

Code-switched conversations

Domain terminology

Numbers and alphanumeric strings

Then score the systems on six dimensions.

Accuracy

Measure WER, but separately track critical entities.

Terminology

Measure names, products, acronyms, medical terms, account numbers, and reference IDs.

Language behavior

Measure language detection, code-switching, accents, dialects, and regional pronunciation.

Real-time behavior

Measure first-token latency, finalization latency, transcript stability, and interruption behavior.

Scale

Run the same test at 10, 100, 500, and 1,000 concurrent streams where your workload requires it.

Business outcome

Finally measure what the transcript actually enables.

Intent classification

Call routing

Agent assist

Search

Summarization

Voice-agent task completion

Clinical documentation

This final layer often determines the real winner.

The biggest mistake is choosing an ASR model before defining the speech problem

There is no universal winner in speech-to-text.

The right architecture depends on the speech distribution you need to support.

A global media company may prioritize multilingual recognition and dialect coverage.

A financial-services contact center may prioritize alphanumeric accuracy, phone audio, speaker separation, and regional languages.

An Indian consumer application may prioritize code-switching and Indic speech.

A healthcare company may prioritize medical terminology and clinical accuracy.

A voice-AI company may prioritize sub-second latency, concurrency, and downstream intent extraction.

These are different optimization problems.

That is why the most useful comparison between Shunya and Speechmatics is not a feature checklist.

It is a question of which platform lets you optimize for the speech problem you actually have.

Final words

Speechmatics is a strong production speech platform with genuine strengths in multilingual ASR, accents, dialects, real-time transcription, diarization, code-switching, medical speech, and flexible deployment. Its new Melia 1 model makes the multilingual comparison even more competitive.

Shunya’s differentiation is broader than language count.

It is the combination of specialized models and production-scale speech infrastructure.

Zero STT Universal handles broad speech.

Zero STT Indic is optimized for 55+ Indian languages and regional speech.

Zero STT Codeswitch is designed for mixed-language conversations.

Zero STT Med is built around healthcare speech and terminology.

Above that, Shunya adds a speech intelligence layer that can turn audio into transcript, intent, sentiment, emotion, and speaker information.

And on the infrastructure side, Shunya publishes 3.10% composite WER, 146× real-time throughput, sub-500ms streaming performance, and 240+ concurrent streams per GPU.

The strongest reason to consider Shunya is therefore not:

“It supports more languages.”

It is:

“It gives us a speech stack that can be specialized around the way our users actually speak.”

For an Indian contact center, that might mean Indic and code-switched models.

For a healthcare platform, it might mean specialized medical recognition.

For a voice-AI application, it might mean latency, concurrency, and speech intelligence.

For a private deployment, it might mean on-premise or CPU-compatible inference.

And for an enterprise processing millions of minutes of audio, it may be the difference between simply transcribing conversations and building an intelligence layer on top of them.

The best evaluation is still the simplest one:

Take your hardest production recordings.

Run them through both platforms.

Measure not just WER, but critical-word accuracy, code-switching, latency, concurrency, and downstream business outcomes.

Because the best ASR system is not the one with the best marketing benchmark.

It is the one that makes your actual product work better.

Contact us to know more.

Frequently asked questions

Is Shunya better than Speechmatics?

Neither platform is universally better. Speechmatics is especially strong in global multilingual ASR, accents and dialects, real-time transcription, and flexible deployment. Shunya differentiates through specialized speech models, Indian-language depth, code-switching, high-concurrency inference, speech intelligence, and a broader voice-AI stack.

Which supports more languages, Shunya or Speechmatics?

Shunya currently markets 216+ languages, while Speechmatics supports 56+ languages. Shunya’s technical documentation distinguishes a 204-language universal model from its 55+ language Indic model, so enterprises should evaluate the specific model relevant to their workload rather than relying only on headline language counts.

Does Speechmatics support code-switching?

Yes. Melia 1 supports automatic language detection and code-switching across 56+ supported languages. Speechmatics has also published recent code-switching evaluations across Arabic, Mandarin, Tamil, and English.

Does Shunya support code-switching?

Yes. Zero STT Codeswitch is specifically designed for mixed-language speech such as Hinglish and Tanglish.

Which is better for Indian languages?

Both platforms support Indian languages. Shunya’s main differentiator is its dedicated Indic model strategy, covering 55+ Indian languages and regional dialects, alongside a separate code-switching model for mixed-language speech.

Does Speechmatics support medical transcription?

Yes. Speechmatics offers a dedicated Medical Model for clinical speech, alongside its general speech models.

Does Shunya support medical speech?

Yes. Zero STT Med is specialized for clinical conversations, medical terminology, drug names, procedures, and healthcare workflows.

Which is faster, Shunya or Speechmatics?

Both have strong real-time capabilities. Shunya publishes sub-500ms streaming performance, while Speechmatics describes partial transcription arriving within a few hundred milliseconds. The meaningful comparison should be performed on the same audio and network conditions.

Which is cheaper?

The answer depends on workload. Speechmatics currently lists Melia 1 batch from $0.129/hour, while Shunya Zero STT starts at $0.0039/minute, or approximately $0.234/hour before volume discounts. Specialized models and downstream intelligence can change the total cost calculation.

Can both platforms be deployed on-premise?

Yes. Speechmatics supports cloud, on-premise, on-device, and hybrid deployments, while Shunya supports cloud, on-premise, air-gapped, and CPU-compatible deployment options in supported configurations.

Which is better for voice agents?

Both can support voice-agent architectures. Speechmatics provides low-latency speech capabilities and real-time APIs, while Shunya combines STT with TTS, voice agents, SLMs, edge speech understanding, and knowledge-grounded voice AI in its broader platform.

How should I compare Shunya and Speechmatics?

Use your own production audio and measure WER, critical terminology, code-switching, diarization, first-token latency, finalization latency, concurrency, and the business outcome your speech system is expected to deliver.