- The paradox of the audio revolution
- Technological foundation: Evolution of voice technologies
- Key metrics for decision making
- Opportunities for practical implementation
- Global arena: Who is who in the STT/TTS market
- How specialized services change the rules?
- What awaits us: Voice employees instead of voice assistants
The paradox of the audio revolution
We live in the era of text messages. Messaging in WhatsApp, Telegram, Facebook has replaced calls. It’s convenient: write, send, find the needed message in the history, copy the text, and forward it to colleagues. But, simultaneously, audio content is developing like never before:
- Podcasts grow by 25% annually and are already listened to by 2 billion people;
- YouTube has transformed from a video platform to an audio platform — half of the users listen to it in the background, without looking at the screen;
- Voice messages have become so commonplace that some people do not want to type long texts anymore.
What’s happening? Why, in the text era, audio is not only holding on but also conquering new territories?
Where audio beats text
Despite all the advantages of chats, audio has a clear advantage:
- Multitasking: you can listen while driving, cooking, working out;
- Emotionality: it’s extremely hard to assess the tone of the interlocutor in a chat. The voice immediately makes it clear what the person meant. The manner of speaking and the pace convey more information than the most accurate words;
- Speed of Consumption: A person speaks at a speed of 150-200 words per minute, reads to themselves about 250. But can understand by listening up to 400 words per minute — twice as fast as reading. The brain processes audio streams more efficiently than visual text;
- Trust: in the era of deepfakes and AI texts, voice still seems more trustworthy. Faking intonation and naturalness of speech is harder than generating convincing text.
These are significant arguments in favor of audio content. But there’s one problem — voice is inconvenient for search, analysis, and structuring. You can find a needed message in a chat by keywords in seconds, but to find a specific phrase in an hour-long conversation recording, you’d have to listen to the entire recording.
Therefore, businesses are stuck in a paradox: on one hand, everyone understands that voice communications contain more information and emotions. On the other — technically it’s difficult to work with this information. Result: thousands of hours of valuable conversations with clients turn into digital trash, which is impossible to analyze and use for the company’s development.
STT (Speech-to-Text)/TTS (Text-to-Speech) technologies solve this problem, combining the best of both worlds: preserving the richness of voice communication while making it as convenient to work with as text.
Technological foundation: Evolution of voice technologies
In the past, voice technologies were more of a problem than a solution. Systems constantly made errors, confused words, didn’t understand accents. Companies did not consider them for use, as there was too much error and too little benefit. Now, voice technologies are not just a convenient feature but a full-fledged tool for automation and analysis.
Speech-to-Text (STT): From voice to text
Previously, speech recognition systems worked primitively — analyzing individual sounds without understanding the context. The percentage of incorrectly recognized words (WER) reached 25-30%, which made automation impossible.Modern neural networks are based on the transformer architecture — the same technology that underpins ChatGPT. They analyze not individual sounds, but whole phrases in context. If a client says “I want to cancel my subscription,” the system understands the intention, not just deciphering the words.STT can solve many tasks, leading to optimization of business processes:
Agent ↔ Client (control and analytics):
- Quality control: The system analyzes each call and highlights problem moments — rudeness of the manager, speech tempo, decrease in customer loyalty, violation of sales scripts;
- Speech analytics: Identifying trends in client requests, analyzing the effectiveness of scripts, finding reasons for purchase refusals;
- Real-time hints: While the client speaks, the system suggests relevant information, objections, deal closing techniques to the manager.
Robot ↔ Client (full automation):
- Intelligent voice menus: Instead of “press 1 for sales department” the client just speaks their request, and the system understands;
- Automatic resolution of requests: Checking balance, order status, changing tariffs — all without agent intervention;
- Voice consultant bots: AI answers 80% of typical questions with a voice indistinguishable from a human’s.
Text-to-Speech (TTS): From text to voice
In the 1990s, synthesized speech sounded too robotic. It was easy to discern that a machine was speaking. Modern systems like Google’s WaveNet and Baidu’s Tacotron create speech almost indistinguishable from a human’s. The Mean Opinion Score (MOS) — a subjective assessment of speech quality — reaches 4.5 out of 5, matching that of a professional speaker.
Which business tasks can be improved with TTS:
- Smart IVR: Instead of “press 1 for the sales department,” the client simply states their need. The system understands the request and immediately connects with the appropriate specialist;
- Personalized dialer: The system can call a thousand clients with unique offers using a voice that sounds like a living person;
- Multilingual service: A single agent, with the help of an AI assistant, can serve clients in different languages through speech synthesis;
- Notifications and reminders: Automated calls about order status, overdue payments, doctor’s appointments;
- Unique brand voice: Creating a company’s proprietary voice. For example, Netflix uses a unique voice for its trailers, McDonald’s for drive-through, banks for serious and confidential messages.
Key metrics for decision making
From the vast array of neural networks for speech recognition available on the market, you need to find the one that suits your business. For comparison, you can use key metrics.
Word Error Rate (WER)
WER – a metric for measuring the percentage of incorrectly recognized words. The decrease in the percentage of WER fell between 2010 and 2020. Significant improvements in automatic speech recognition occurred thanks to “Deep Learning” technologies – a subdivision of machine learning where multi-layer neural networks are used.This indicator determines whether the system can be trusted with tasks:
- WER up to 5% – critical processes can be automated (order acceptance, technical support, financial operations);
- WER 5-10% – suitable for assisting agents (hints, preliminary processing);
- WER above 15% – unacceptable for critical tasks.
Latency
Delay (Latency) — the time between an action and the system’s response. This indicator is important because the human brain expects an immediate reaction in dialogue. A delay of more than 300ms destroys the feeling of a natural conversation — the client starts to think that the system “froze” or didn’t hear them.For interactive scenarios, processing time is critical:
- 200-300ms — excellent performance, delay is unnoticeable;
- 300-500ms — normal, people expect responses within 300-500 milliseconds. The upper limit of natural perception. Suitable for most business tasks;
- 500-800ms — Noticeable delay, the overall goal for voice-to-voice interactions — 800ms for the entire system. If only STT takes 500-800ms, then the delay exceeds comfortable limits;
- Above 800ms — Unacceptable delay. Not suitable for critical tasks.
A prolonged delay in response negatively affects the level of service, as it simply annoys and the system seems broken. If your STT system works slowly, customers will demand “to be connected with a human” instead of resolving questions through a voice assistant.
Opportunities for practical implementation
Choosing a speech recognition system is not just about comparing accuracy and price. It’s important to understand what specific opportunities will help solve business tasks and whether they are available in the speech recognition system’s arsenal.
Basic functions
- Real-time recognition (streaming) – processes the audio stream without buffering the full recording. The system returns intermediate results at intervals of 100-200ms and final results at the end of sentences. Important for voice-to-voice applications and interactive systems;
- Model retraining (domain adaptation) – adapts the acoustic and language models to specific terminology. The system can be retrained based on texts from the subject area or audio recordings with markup. Increases the accuracy of recognizing industry terms by 15-30%;
- Confidence scoring – the system evaluates the quality of its work for each recognized word. Returns a number from 0 to 100%, where 95% means “almost sure,” and 30% — “probably made a mistake.” In case of low confidence, the system can provide several options: “bank” (60%), “can” (25%), “punk” (15%). This allows sending doubtful fragments for human verification.
Additional capabilities
- Speaker diarization – automatically determines the number of participants in the conversation and assigns each audio segment to a specific speaker. The algorithm analyzes voice characteristics and groups segments with similar sounds;
- Automatic punctuation – uses language models to restore punctuation marks and capital letters in the recognized text. The system analyzes speech features (pauses, intonation) and context to make decisions about punctuation placement;
- Emotion analysis – determines the mood of the speaker by voice, tone, speech pace, pauses. It recognizes how a person pronounces words and classifies emotions: “neutral,” “joy,” “irritation,” “sadness.” Gives results in percentage value. Useful for call centers — can automatically identify dissatisfied clients.
Special features
- Noise reduction – applies spectral subtraction algorithms or deep neural networks to filter out background noise. Effective for audio with a low signal/noise ratio (less than 10dB SNR);
- Multilingual recognition – supports automatic language detection (language identification) or switching between predefined languages within a single session. The system can handle utterances when a person switches between languages during a conversation (code-switching);
- Timestamp alignment – links each word to an exact time in the audio recording with precision up to 10-50ms.
What to pay attention to when choosing features:
- Processing speed requirements: immediate response (less than 200ms), quick response (up to 1 second), or batch file processing is acceptable;
- Recording quality: telephone quality (8 kHz), studio quality (44 kHz), whether there is background noise, whether you use sound compression;
- Conversation specifics, how many specialized terms are there, are there accents, what languages do users speak;
- The ability to train the speech recognition system with unique terminology.
Based on these indicators, you can choose the most suitable speech recognition system.
Global arena: Who is who in the STT/TTS market
+OpenAI Whisper: Multilingual champion
- WER: 8.06% — the best performance on the market, of course, it varies depending on the language, but Whisper maintains its leadership. Even in 2020, such accuracy seemed unattainable even for the English language.
- Languages: understands 99 languages — from popular European to exotic African dialects. WER for English — 5–8%, Ukrainian — 15–39%, Spanish and German — 7–12%.
- TCO: $218,700/year vs $38,880 for Google (price paradox).
- Limitations:
- Hallucinations — the system may “invent” words with poor audio quality or long pauses. Causes difficulties in medicine and jurisprudence;
- Only batch processing — no API for real time (for real-time see GPT-4o-transcribe below). Cannot be used for agent hints during a call. Maximum audio length — 30 seconds per request;
- Hardware requirements — Whisper requires powerful hardware. Minimum — a video card, optimally — a professional one. For large tasks, a cluster of 4–8 such cards is needed; energy consumption of one is like a heater ($200–400 per month).
Whisper suits companies with their own IT infrastructure and high accuracy requirements. Not suitable for startups and tasks requiring real-time processing.
+GPT-4o-transcribe: The new generation from OpenAI
OpenAI has released a new model, gpt-4o-transcribe, with improved characteristics.
Features:
- Surpasses Whisper v2 and v3 in accuracy across all languages;
- Native support for streaming recognition in real time;
- Built on the GPT-4o architecture, not a specialized voice architecture;
- Better handling of accents, noise, and various speech speeds.
TCO: through OpenAI API at a price of $0.006 per minute of audio or the GPT-4o Mini Transcribe version at $0.003 per minute. Payment is on a pay-as-you-go basis.
Companies can integrate it into their products via API and use it for transcription in real time. Also applicable in call centers, subtitle systems, voice assistants with the ability to process audio files of any size.
Limitations:
- Cloud-only solution (cannot be deployed on own servers);
- A OpenAI or Azure account is required;
Any company can start using gpt-4o-transcribe today — just obtain API keys from OpenAI or connect through Azure.
+AssemblyAI Universal-2: The new king of accuracy
- WER: 6.6% for the English language — this is better than Whisper by 1.5%. The system was specifically created for business applications: call centers, medicine, sales, jurisprudence. Universal-2 is optimized for real-life conditions with noise, accents, and telephone quality audio.
- Languages: Focuses on quality, not quantity — supports 12+ main languages with high accuracy. English WER 6.6%, Spanish 8-12%, French 9-14%, German 10-15%. Each language is meticulously optimized for business lexicon.
- Built-in business analytics: The main competitive advantage — ready-to-use tools out of the box. Identifying speakers with 85-92% accuracy, real-time tone analysis, automatic key topic highlighting, and compliance with scripts monitoring.
- TCO: $0.37/hour for the full version, $0.12/hour for Nano — transparent pricing without hidden fees and minimum commitments. 5-6 times cheaper than Whisper with comparable quality.
- Advantages:
- Real-time processing — WebSocket API with 200-400ms latency for agent hints during a call;
- Ready integrations — connectors with popular CRMs (Salesforce, HubSpot), no need for months of development;
- 99.9% uptime — with SLA guarantees, suitable for critical business processes;
- Cloud solution — does not require expensive hardware, set up in a couple of days.
- Limitations:
- Fewer languages — compared to Whisper’s 99 languages, support is limited to main European languages;
- Cloud-only solution — no option to deploy the system on own servers, which may be critical for banks, medical organizations, and government structures with strict data protection requirements.
- Vendor lock-in — tying to the AssemblyAI ecosystem may create problems when switching providers.
AssemblyAI Universal-2 — the optimal choice for most business tasks. Combines high accuracy, reasonable price, and ready-to-use tools for analysis. Ideal for companies needing fast results without significant IT investments.
+Google Speech-to-Text: Proven stability
- WER: 16.51%-20.63% — worse than the new leaders, but stable and predictable. Google sacrifices accuracy for reliability and scalability.
- Languages: 125 languages — the widest coverage on the market. Includes rare languages and dialects not supported by anyone else.
- Cost: $0.016/min for real time, $0.002/min for batch processing — among the lowest prices on the market. No hidden charges for additional features.
- Advantages:
- 99.9% uptime — proven by billions of Android devices, operates without failures for years;
- Automatic scaling — withstands any loads without pre-configuration;
- Managed service — Google takes care of all infrastructure and updates issues.
- Limitations:
- Lower accuracy — for critical applications, additional processing might be required;
- Limited customization — difficult to adapt to company-specific terminology.
Google — the choice for companies needing stability at high processing volumes and low quality requirements.
+Microsoft Azure Speech: Enterprise integration
- WER: 18-22% — comparable to Google, but with unique business features not available from competitors:
- Custom Neural Voice — creating a personal voice.
- Emotional TTS — the system changes intonation depending on the situation.
- Speaker Recognition — biometric identification of a client by voice.
- Medical specialization — understanding medical terminology.
- Languages: 100+ languages with a focus on corporate use. Especially strong in European languages for business communications.
- Advantages:
- Deep integration with Microsoft — works out of the box with Office 365, Teams, Dynamics CRM;
- Enterprise focus — addresses corporate tasks, not just speech recognition;
- Flexible deployment models — cloud, hybrid or on-premises.
- Limitations:
- Dependence on Microsoft ecosystem — maximum benefit only when using other MS products;
- Complex set-up — requires expertise for full utilization of capabilities.
Azure — the perfect choice for companies already operating within the Microsoft ecosystem.
+Amazon AWS (Transcribe + Polly): Flexibility in customization
- WER: 18-25% depending on conditions. Not the highest accuracy, but compensated by customization flexibility.
- Polly TTS: 100+ voices, 4 synthesis engines, average expert quality evaluation (MOS) above 4.5 — one of the best TTS services on the market.
- Languages: 31 languages for Transcribe, 60+ languages for Polly. Less than Google, but higher quality.
- Unique features: Custom Vocabulary for industry terminology, Speaker Diarization to identify speakers, medical specialization understanding medical terms.
- Advantages:
- Modularity — use only the components you need;
- AWS ecosystem — easy integration with other Amazon services;
- Flexible rates — pay only for what you use.
- Limitations:
- Complex architecture — you need to manually link different services;
- Requires technical expertise — not a plug-and-play solution.
AWS — the choice for companies with a strong IT team that want to customize the solution for their tasks as much as possible.
+NVIDIA Parakeet: Technical superiority
- WER: 6.05% — leads in the HuggingFace ranking, surpassing even Whisper in accuracy. This is the result of powerful computing resources from NVIDIA and focus on technical perfection.
- Languages: 100+ languages with a focus on technical quality. Each language undergoes thorough optimization on NVIDIA GPU clusters.
- Features: Processing super-long records up to 11 hours without loss of quality — a unique capability in the market. Most systems are limited to 30 seconds or a few minutes.
- Open-source license: Full access to code, possibility for modification for specific tasks, absence of vendor lock-in.
- Advantages:
- Full control — can be adapted to any company requirements;
- No volume limitations — process as much as needed without extra charges for limits;
- GPU optimization — most efficient use of video card capacities.
- Limitations:
- Requires a serious IT team — needs ML engineers for deployment and support;
- High infrastructure costs — own GPU servers or expensive rental of cloud capacities;
- No ready business analytics — all additional functions need to be developed independently.
Parakeet — the choice for technology companies with their own ML teams who need maximum accuracy and control.
+iFlyTek: Asian leader
- WER for Chinese: <5% — the best result in the world for the Chinese language and its dialects. Western systems show 15-25% for Chinese.
- Specialization: Deep expertise in tonal languages (Chinese, Vietnamese, Thai), understanding hieroglyphic writing, and cultural specifics of the Asian business.
- Unique capabilities: Recognition of mixed Chinese-English speech, understanding regional dialects, specialized models for education and medicine.
- Advantages:
- Monopoly in the Chinese market — if you’re working with China, there’s practically no alternative;
- State support — massive R&D investments from the Chinese government;
- Deep understanding of Asian languages — takes into account tonality, context, cultural specifics.
- Limitations:
- Limited availability — difficulties using it outside of China due to geopolitical restrictions;
- Weakness in European languages — focus on the Asian region at the expense of global coverage;
- Language barrier — documentation and support mainly in Chinese.
iFlyTek — the unrivaled choice for businesses connected with China and Asian markets. For other regions, there are more convenient options.
How specialized services change the rules?
Previously, we reviewed platforms from technology giants — Google, Amazon, Microsoft, OpenAI. It seems logical to assume that most companies will choose them. However, statistics tell the opposite, many medium-sized businesses prefer specialized STT/TTS services to universal platforms. The reason is simple — for most business tasks, specific functions are needed, not a full package of services.
Specialized STT/TTS services
ElevenLabs: Developed its own neural network based on transformers, specifically for emotional speech synthesis. Uses contextual embeddings to understand emotions from text. The service can clone a voice in 1 minute of recording, MOS 4.8/5, intonations change depending on context, but the system does not learn new words or specific terminology. TTS robots are almost indistinguishable from humans. Suitable for multilingual campaigns and can adapt to emotions. Downsides are: only 29 languages and cloud use only. STT works only within projects, without real-time and incoming call analysis.
Deepgram: The technology is built on its own End-to-End Deep Learning architecture and is a streaming-first neural network optimized for GPU.The service processes speech with minimal delay of 150–200ms, perceived by the brain as “instant”.
Recognition starts from the first word, possible Edge deployment — operation without internet, and predictive transcription guesses the ends of phrases with 85% accuracy. The system scales to 1000 parallel streams. Downsides are: WER 10–14% (10–14 errors per 100 words), supports only 12 languages. You can retrain the STT for your own terminology and dictionary through API, TTS is basic, voice customization is limited.
Deepgram can be used for real-time hints for agents, instant alerts for supervisors, processing 1000+ concurrent calls. Limitations — low accuracy. Basic TTS exists, but sounds synthetic, thus not suitable for premium service, appropriate for technical notifications.
Murf AI: Uses licensed models (WaveNet, Tacotron) with its own processing layer, focusing on UX. Advantages: voice training, visual editor with drag-and-drop pauses, 120+ voices with different emotions and accents, team collaboration, built-in library. Downsides: no full API, MOS 4.3, limited customization – no way to add new words or corporate lexicon, depends on internet. In a call center, Murf is suitable for Text-to-Speech: quick IVR without developers, a wide choice of voices. STT is missing.
Sonix: Uses Amazon Transcribe, Google Speech-to-Text and Microsoft Azure models as a base, adding a powerful layer of post-processing and collaboration. Advantages: collaborative editing of transcripts, AI analysis of themes and emotions, 15+ formats of export, full-text search, version history. Downsides: WER 15–20%, no real-time, expensive storage, dependence on Amazon. No support for custom terminology. Suitable for Speech-to-Text in call centers: QA, call analysis, pattern searching. TTS is missing — purely analytical tool.
Specialized services are relevant because they solve specific business tasks better than universal platforms, as they focus on one direction and rapidly develop it. For companies where the quality of a specific function — be it speech synthesis or recognition — is critical, such an approach provides an advantage and significantly saves budget.
What awaits us: Voice employees instead of voice assistants
We are on the threshold of an era when AI will no longer be just “smart search” and become an active participant in work processes. Voice technologies are the key to this transformation because speech remains the most natural way of human communication. What can we expect in the near future?
- AI employees in messengers: Soon there will be services with a full voice interface right in Telegram, WhatsApp, Discord. These won’t be primitive chatbots, but virtual employees, capable of participating in group discussions, conducting presentations, moderating conference calls. Imagine an AI analyst joining a meeting, answering data questions in real time, and immediately drafting an action plan.
- Personal experts for everyone: Services like NotebookLM are just the beginning. Soon, every coach, teacher, tutor will be able to create their voice double, scaling their presence worldwide. A single English language specialist from London could simultaneously tutor a thousand students, maintaining a personalized approach and unique methodology.
- New profession: AI dialog analyst: When AI becomes a full-fledged participant in business conversations, there will be a need for specialists to analyze such “hybrid” human-AI dialogs. How does AI influence decision-making? What behavior patterns does it form in people? This is a separate industry of the future.
Practical application right now:
- Telegram bots with a voice interface for corporate tasks;
- WhatsApp Business with AI consultants, indistinguishable from live employees;
- Discord servers with AI moderators, who understand context and emotions.
Companies that start experimenting with voice AI employees now will gain a huge advantage when these technologies become mainstream.
Conclusion
The market for voice technologies has passed the point of no return. WER dropped from 25-30% to 6-8%, latency reduced to 150-200ms, and the quality of synthesis reached MOS 4.8, practically indistinguishable from a human. This is not just technical progress, it’s a paradigm shift: voice turned from a problem into an advantage.
As it turned out, universal platforms are not always better than specialized solutions for specific tasks. Google processes 125 languages but with a WER of 16.5%. AssemblyAI works with 12 languages but provides a WER of 6.6%. Deepgram sacrifices accuracy for a speed of 150ms. ElevenLabs ignores STT, yet their robots cannot be distinguished from humans. Each has chosen its superpower and perfected it.
The practical conclusion for business is simple: don’t look for one solution for everything. Use different services and combine capabilities according to priorities — speed, accuracy, voice quality, or ease of implementation. A modular approach, where each task is addressed with the optimal tool, saves budget while significantly improving results. Start with a pilot project on one critical task, assess ROI in 2-3 weeks, then scale the successful experience. It’s more effective than months of setting up a universal platform, which ends up doing everything mediocrely.
