Why Shunya beats Azure Speech for production speech-to-text

Azure Speech is a mature enterprise speech platform. It supports real-time and batch transcription, fast transcription, speaker diarization, language detection, phrase lists and Custom Speech models, and it fits naturally into the wider Microsoft Azure ecosystem.

That makes Azure a strong choice for many enterprise teams.

But speech recognition gets more complicated when the requirements move beyond a generic transcription API.

Indian languages. Code-switching. Real-time conversations. Domain terminology. Speech intelligence. Private deployment. Scale.

This is where Shunya takes a different approach.

Shunya Zero STT is built around 216+ languages, 55+ Indian languages, sub-500ms first-token positioning, 240+ concurrent streams per GPU, speaker diarization and speech intelligence, with specialized models for Indic, code-switched and medical speech.

The real question is not whether Azure Speech is capable.

It is:

Which speech-to-text platform gives you the right combination of accuracy, language depth, latency and speech intelligence for your production workload?

Shunya and Azure Speech: at a glance

Azure Speech and Shunya are both managed speech platforms, so the decision comes down to the capabilities that matter most to your application.

Aspect	Shunya Zero STT	Azure Speech
Platform	Enterprise speech-to-text platform	Microsoft Azure Speech
Deployment	Cloud and enterprise private deployment options	Azure cloud, with additional deployment options across the Azure Speech ecosystem
Pricing	From $0.0039/min for Zero STT batch	Usage-based; varies by model, feature and commitment
Key strength	Multilingual speech, Indic depth and speech intelligence	Enterprise cloud integration and Custom Speech
Languages	216+	Broad language and locale support, varying by model
Indian languages	55+	Multiple Indian languages
Real-time	Sub-500ms first-token positioning	Real-time transcription with intermediate results
Batch	Yes	Yes
Speaker diarization	Supported	Supported
Language identification	Supported	Supported
Custom vocabulary	Supported	Phrase lists + Custom Speech
Code-switching	Dedicated model	Multilingual capabilities depend on model/configuration
Speech intelligence	Intent, sentiment, emotion and more	Available through Azure Speech + broader Azure AI services
Medical ASR	Zero STT Med	Custom Speech / general speech workflows
Best fit	Enterprise speech, Indian languages, voice applications	Azure-native enterprise applications

Azure’s current Speech documentation supports real-time, fast and batch transcription, along with Custom Speech and diarization.

Azure Speech is a serious enterprise ASR platform

Azure Speech has a major advantage that is difficult to ignore:

Microsoft Azure.

For organisations already using Azure, speech can plug into an existing environment for identity, storage, networking, security and application infrastructure.

Azure Speech currently supports:

• Real-time transcription
• Fast transcription
• Batch transcription
• Speaker diarization
• Language detection
• Phrase lists
• Custom Speech
• Profanity filtering
• Multiple SDKs and APIs

Azure also provides Custom Speech, allowing organisations to build models tailored to specific domains and acoustic conditions.

So Shunya does not win simply by having “more ASR features.”

Azure already covers a substantial production feature set.

The more interesting difference is specialization.

Which is more accurate?

Accuracy depends heavily on the audio being transcribed.

A model that performs well on clean English speech may behave very differently on:

• Telephone conversations
• Background noise
• Regional accents
• Indian languages
• Code-switched speech
• Product names
• Medical terminology

Shunya currently publishes a 3.10% composite WER in English across eight OpenASR benchmarks for Zero STT.

Benchmark	Shunya Zero STT
LibriSpeech Clean	0.71%
SPGISpeech	1.10%
TED-LIUM	1.43%
LibriSpeech Other	2.17%
AMI	4.19%
VoxPopuli	4.34%
GigaSpeech	4.99%
Earnings22	5.83%
Composite	3.10%

Azure publishes model- and language-specific performance information rather than one universal Speech to Text WER number that represents every model and workload. Its documentation also makes clear that language and feature availability can vary depending on the speech model being used.

That makes the most useful test straightforward:

Put your own audio through both systems.

If you are building a banking application, test banking conversations.

If you’re building healthcare software, test medical terminology.

If you’re building for India, test Indian languages and code-switching.

The feature gap is smaller than the marketing pages suggest

Azure already handles many features that a production ASR application needs.

Feature	Shunya	Azure Speech
Batch transcription	Built in	Built in
Real-time streaming	Built in	Built in
Fast transcription	Supported	Built in
Speaker diarization	Built in	Built in
Language detection	Supported	Supported
Word-level details	Supported	Supported
Timestamps	Supported	Supported
Confidence scores	Supported	Supported
Custom terminology	Supported	Phrase lists
Custom models	Supported	Custom Speech
Code-switching	Dedicated model	Model/configuration dependent
Indian-language specialization	Dedicated Indic model	Language/model dependent
Sentiment	Speech intelligence	Broader Azure AI services
Emotion	Speech intelligence	Broader Azure AI services
Intent	Speech intelligence	Broader Azure AI services
PII	Supported	Azure ecosystem
Medical speech	Zero STT Med	Custom Speech / general workflows

Azure’s phrase-list functionality is particularly useful for proper nouns, acronyms, domain-specific terminology and uncommon words. Microsoft describes it as a runtime recognition feature that can be used with real-time and fast transcription.

Azure also allows significantly deeper customization through Custom Speech when phrase boosting alone isn’t sufficient.

The important difference isn’t that Azure lacks production features.

It is that Shunya puts more of its differentiation directly into specialized speech models and speech intelligence.

Real-time transcription: both platforms can stream

Real-time transcription isn’t a simple differentiator anymore.

Azure Speech supports real-time transcription with intermediate results, while Shunya provides dedicated streaming recognition and positions Zero STT around sub-500ms first-token latency.

So the useful questions become:

How quickly does the first useful result arrive?

How accurate are partial transcripts?

How does the model behave on noisy calls?

How many concurrent streams can the infrastructure support?

For a contact centre or voice agent, these details can matter more than a simple “streaming: yes” checkbox.

Indian languages are where specialization matters

Azure Speech supports many Indian languages and locales, with its documentation providing language and feature availability by model.

Shunya takes a more focused approach.

Zero STT Indic supports 55+ Indian languages, while Zero STT Codeswitch is designed for mixed-language conversations.

This difference matters because Indian speech is rarely as clean as a language dropdown suggests.

A production application may encounter:

• Regional accents
• Low-resource languages
• Hinglish
• Tanglish
• Transliteration
• Indian names
• English product terminology inside regional-language conversations

Consider:

“Mera account block ho gaya, can you help me reactivate it?”

The challenge isn’t just recognizing two languages.

It’s recognizing them together.

That’s why language coverage should always be evaluated alongside actual transcription quality.

Code-switching is a production problem

A user doesn’t need to announce when they switch languages.

They simply do it.

“Loan ka status check karke please mujhe update kar dena.”

A generic multilingual model may recognize the languages involved.

A production speech system also needs to preserve the meaning, terminology and structure of the conversation.

Shunya’s Zero STT Codeswitch is specifically designed for mixed-language speech such as Hinglish and Tanglish and many more.

That becomes especially important when ASR feeds:

intent detection → routing → automation

A small ASR mistake can change the downstream intent.

So code-switching is not just a language feature.

It can affect the entire application.

Custom Speech is where it might be interesting

Azure Custom Speech allows organisations to train and deploy models for domain-specific scenarios, including improving recognition for specialised vocabulary and acoustic conditions.

Before building a custom model, Azure also provides phrase lists for lightweight terminology boosting.

Microsoft recommends phrase lists for words such as:

• Names
• Locations
• Acronyms
• Product names
• Industry terminology

This gives Azure a useful progression:

base model → phrase list → Custom Speech

Shunya approaches domain specialization through its model family and enterprise customization.

That means the right choice depends on how much model customization your workload actually needs.

For a relatively small vocabulary list, Azure phrase lists may be enough.

For a specialized speech workload where language or domain behavior is the primary problem, Shunya’s dedicated model approach can be more relevant.

Speaker diarization: both platforms support it

Azure Speech supports speaker diarization and can identify up to 35 speakers in a recording according to its current documentation.

Shunya also supports speaker diarization within Zero STT.

So diarization is not a reason by itself to choose one platform.

The better test is:

How accurately do they separate your speakers?

Especially when recordings contain:

• Crosstalk
• Multiple participants
• Telephone audio
• Background noise
• Overlapping speech

That’s where real-world testing matters more than the feature list.

Pricing: Azure Speech vs Shunya

Both platforms use usage-based pricing, but the pricing structures are different.

Shunya currently lists Zero STT starting at $0.0039/min for batch transcription, with separate pricing for specialized models.

Azure Speech’s pricing varies by transcription method, model and usage configuration. Microsoft currently lists standard, custom and enhanced speech-to-text categories, including real-time, fast and batch transcription.

Azure can also offer different pricing structures through commitment tiers and other purchasing options.

That means the meaningful comparison isn’t just the advertised per-minute number.

Calculate:

monthly audio volume + model + transcription mode + features + channels + commitment

For enterprise workloads, those details can materially change the final cost.

The Microsoft ecosystem is a real advantage

This is where Azure can be the better choice.

If your application already uses:

Azure Storage

Azure Kubernetes Service

Azure AI services

Microsoft Entra ID

Microsoft data and analytics

then Azure Speech can fit naturally into the architecture.

You aren’t just buying an ASR API.

You’re adding speech to an existing cloud platform.

For organisations standardised on Microsoft infrastructure, that can reduce integration complexity and simplify governance.

Shunya’s advantage is different.

It is a speech-focused platform that can connect STT with TTS, Voice Agents, Small Language Models, Edge SLU and Knowledge Graph capabilities rather than tying the speech layer to one cloud ecosystem.

The real question is what you need after transcription

Suppose your application only needs:

audio → transcript

Azure Speech may be all you need.

But consider a contact-centre application:

audio → transcript → speaker → intent → sentiment → action

Or a healthcare workflow:

audio → transcript → medical terms → structured information

Or a voice agent:

audio → transcript → reasoning → response → speech

This is where the broader speech architecture matters.

Shunya’s Zero STT platform exposes transcription alongside speech intelligence features such as intent, sentiment, emotion and speaker labels.

Azure can achieve many of these workflows by combining Speech with other Azure AI services.

That’s a strength of the Microsoft ecosystem.

But it also means your architecture may span multiple services.

When Shunya makes more sense

Shunya becomes more compelling when:

Indian speech is central to the application

You need dedicated Indic speech models and 55+ Indian languages.

Code-switching is common

Your users naturally mix English with Indian languages.

Speech intelligence is part of the product

You need intent, sentiment, emotion and speaker information alongside the transcript.

You need specialized speech models

Healthcare, Indic and code-switched speech can be handled through dedicated model variants.

You want a speech-focused stack

You want STT to connect naturally with TTS and voice-agent infrastructure.

You need production speech beyond a single cloud ecosystem

Shunya provides enterprise deployment options for organizations with specific infrastructure requirements.

Shunya vs Azure Speech by use case

Use case	Better fit	Why
Azure-native application	Azure	Deep Microsoft ecosystem integration
General cloud transcription	Both	Both provide managed ASR
Real-time transcription	Both	Both support streaming
Indian-language application	Shunya	Dedicated Indic model + 55+ languages
Hinglish application	Shunya	Dedicated code-switching model
Domain vocabulary	Both	Azure phrase lists / Custom Speech; Shunya specialization
Speaker-labelled calls	Both	Both support diarization
Speech intelligence	Shunya	Intent, sentiment, emotion and related capabilities
Custom model training	Azure	Mature Custom Speech workflow
Medical speech	Shunya	Dedicated Zero STT Med
Microsoft enterprise stack	Azure	Native Azure integration
Speech-focused voice stack	Shunya	STT + TTS + Voice Agents + SLMs + Knowledge Graph

Can you use Azure Speech and Shunya together?

Yes.

A hybrid approach can make sense when different workloads have different requirements.

For example:

Azure Speech

→ Azure-native applications
→ Existing Microsoft infrastructure
→ Custom Speech workflows

Shunya

→ Indian-language applications
→ Code-switching
→ Specialized speech models
→ Speech intelligence
→ Production voice workflows

The goal isn’t necessarily to put every audio workload behind one API.

It’s to use the right speech model for the job.

How should you evaluate them?

Don’t make the decision from a feature checklist alone.

Take representative audio from your application and test both platforms.

Include:

Indian languages

Regional accents

Code-switched speech

Telephone calls

Background noise

Multiple speakers

Industry terminology

Names and product names

Then compare:

Metric	What to test
WER	Overall transcription accuracy
Entity accuracy	Names, brands and terminology
Number accuracy	Dates, amounts and identifiers
Code-switch accuracy	Mixed-language conversations
Diarization	Speaker attribution
First-result latency	Responsiveness
Final latency	End-to-end performance
Concurrent streams	Production capacity
Cost per hour	Actual usage cost
Downstream accuracy	Intent and classification

The right ASR platform is the one that performs reliably on your audio.

Final words

Azure Speech is a mature enterprise speech platform.

It provides real-time, fast and batch transcription, speaker diarization, language detection, phrase lists and Custom Speech, with the broader Azure ecosystem behind it.

For teams already invested in Microsoft Azure, that is a significant advantage.

Shunya takes a more speech-specialized approach.

Zero STT combines 216+ languages, 55+ Indian languages, sub-500ms first-token positioning, 240+ concurrent streams per GPU and speech intelligence, alongside dedicated models for Indic, code-switched and medical speech.

The difference isn’t that Azure can’t handle production speech.

It can.

The difference is where each platform puts its emphasis.

Azure gives you speech as part of the Microsoft cloud.

Shunya gives you speech as a specialized production layer.

For Azure-native applications and teams that need Custom Speech, Azure can be the natural choice.

For applications where Indian languages, code-switching, speech intelligence and specialized speech workloads are central, Shunya can be the better fit.

The right question isn’t:

“Which ASR API is better?”

It’s:

“Which speech platform performs best on the languages, audio and workflows my product actually depends on?”

Frequently asked questions

Is Shunya better than Azure Speech?

It depends on the workload. Azure Speech is a strong choice for Azure-native enterprise applications, Custom Speech and broad cloud integration. Shunya is particularly differentiated around Indian languages, code-switching, speech intelligence and specialized speech models.

How many languages does Azure Speech support?

Azure’s overall language and locale support varies by speech capability and model. Microsoft maintains a language-support matrix covering speech-to-text, text-to-speech, pronunciation assessment, translation and other features.

How many languages does Shunya support?

Shunya currently markets Zero STT across 216+ languages, with 55+ Indian languages supported through its Indic speech offering.

Does Azure Speech support Indian languages?

Yes. Azure Speech supports multiple Indian languages and locales, with availability depending on the model and capability.

Does Shunya support Indian languages?

Yes. Zero STT Indic supports 55+ Indian languages.

Does Azure Speech support speaker diarization?

Yes. Azure Speech supports speaker diarization and currently documents support for up to 35 speakers in an audio recording.

Does Shunya support speaker diarization?

Yes. Speaker diarization is part of Zero STT’s published capabilities.

Does Azure Speech support custom vocabulary?

Yes. Azure supports phrase lists that boost the recognition of names, acronyms, domain-specific terms and uncommon words. Microsoft also provides Custom Speech for deeper model customization.

Can Azure Speech handle real-time transcription?

Yes. Azure Speech supports real-time transcription with intermediate results.

Does Shunya support real-time transcription?

Yes. Shunya provides streaming recognition and publicly positions Zero STT around sub-500ms first-token latency.

Which is cheaper, Azure Speech or Shunya?

It depends on your workload. Shunya currently starts at $0.0039/min for Zero STT batch transcription. Azure pricing varies by model, transcription mode, features and purchasing configuration.

Does Azure Speech support code-switching?

Azure supports multilingual speech configurations, but language and model behavior varies by configuration. Shunya provides a dedicated Zero STT Codeswitch model for mixed-language speech.

Which is better for Hinglish?

Shunya is the more specialized choice because it provides a dedicated code-switching model designed for mixed-language speech.

Does Shunya have a medical speech model?

Yes. Zero STT Med is a dedicated model for medical and clinical speech.

Does Azure have Custom Speech?

Yes. Azure Custom Speech allows organizations to create speech models tailored to specific domains and acoustic conditions.

Which is better for Azure-based applications?

Azure Speech is usually the natural starting point when your application already runs heavily on Azure and you want speech integrated with Microsoft’s broader cloud services.

Which is better for Indian voice applications?

Shunya can be the stronger fit when Indian languages, code-switching and regional speech are central requirements because of its dedicated Indic and code-switching model families.

Can I use Azure Speech and Shunya together?

Yes. Different workloads can use different speech providers depending on language, model, infrastructure and application requirements.