Understanding Voice AI Models: TTS - STT, and VTV

Voice AI technology has revolutionized human-computer interaction by enabling machines to understand, generate, and transform speech in increasingly sophisticated ways. Among these AI-driven technologies, three core models stand out: Text-to-Speech (TTS), Speech-to-Text (STT), and Voice-to-Voice (VTV) synthesis. Each of these models addresses distinct aspects of voice intelligence and serves unique purposes across industries. This article provides a technical and informational overview of these models, their operational principles, applications, and how they compare across key performance metrics.

Speech-to-Text (STT): Turning Spoken Words into Data

Speech-to-Text (STT) converts spoken words into written text by analyzing audio features and predicting word sequences. Modern systems use deep learning models like CNNs, RNNs, and Transformers (particularly self-attention) to achieve high accuracy, even in noisy environments.

What Is Text-to-Speech (TTS) and How Does It Work?

Text-to-Speech (TTS) converts written text into spoken audio by analyzing language structure and synthesizing speech. It uses deep learning models like Tacotron, WaveNet, or Transformers to create natural, human-like voices through techniques such as concatenative, parametric, or neural synthesis.

Voice-to-Voice (VTV): The Next Step in Voice Synthesis Technology

VTV are a class of models that are directly trained on voice data and understand voice natively. They are also able to generate a response in voice rather than text. So instead of having any text going to LLM, a voice goes in and then a voice based answer comes out. No transformation of input voice is necessary

Different approaches to building Voice AI agents 

1. STT -> LLM -> TTS Stack 

In this solution, audio input and output are managed via telephony providers like Plivo, Twilio etc. To start with, Voice input from the call recipient is converted to text first. After that, text is passed to LLM to generate a response. This response is given a voice by the TTS provider and passed on to the telephony provider as an audio response.

Issues with this approach:

1. Latency - The STT → LLM → TTS pipeline adds multiple hops, making conversations feel slow and robotic. Even a 1–2 second lag breaks the natural flow of human-like interaction.

2. Transcription - Accuracy depends on the quality of the STT model, and background noise or accents often reduce reliability, leading to broken responses.

3. Diarization - Differentiating between multiple speakers is hard in call scenarios. The system may confuse agent and customer voices, messing up the context.

4. Interruptions Handling - In case of interruption by the call recipient in the middle of a sentence, the AI agent stops speaking and doesn’t handle concerns well.

5. Difficulty in managing Indian Languages - Since this stack relies on STT to transcribe the audio, it is often inaccurate in transcribing Indian languages, which compounds the problem as the conversation goes on.

2. Voice -> LLM -> Voice Stack 

In this solution, audio input and output are managed similarly to the previous solution. However, the entire response audio from the call recipient is transferred to an Audio Model capable of Natural Language processing and an audio response output is generated.

Issues with this approach:

1. Cost - This approach can be up to 8x expensive compared to previous one as the underlying Audio Model is more expensive. So, it is restricted to very niche use cases.

2. Inaccuracy with Indian Languages - Audio AI models are not accurate in interpreting and processing the nuances of many of the Indian languages.

3. Hunar’s Hybrid Stack 

Hunar has developed its proprietary approach to build a Voice AI solution by creating its own pre and post processing steps on top of available LLMs and Audio Models. This approach solves the issues with the previous approaches to a great extent.

In real conversations, pauses and interruptions can cause AI agents to misinterpret sentence endings or talk over users, harming conversation quality. Hunar’s hybrid voice AI tackles this by using a pre and post-processing step apart from the Voice-to-Voice model that effectively handles interruptions and pauses. The output is then enhanced with both off-the-shelf tools and proprietary algorithms to create natural-sounding speech, providing a robust solution to challenges faced by earlier models.

Comparison of different approaches to build Voice AI solutions

With continuous advancements and new upgrades emerging daily in the voice AI space, it is crucial to set clear benchmarks and evaluate each approach using defined criteria. Below is a detailed comparison to help decision makers make the right choice.

Importance of Selecting the Right Voice AI Based on Use Case, Performance Needs, and Technical Constraints

Selecting the right Voice AI solution whether TTS-STT, VTV, or a hybrid model like Hunar’s critically depends on your specific use case, performance needs, and technical constraints. While TTS is great for converting text to speech and STT excels at transcription, Hunar’s hybrid VTV model offers superior advantages by combining the strengths of all components with advanced proprietary conversational capabilities.

Hunar’s hybrid stack stands out by handling real-time interruptions smoothly, delivering more natural, human-like speech, and providing detailed, context-aware call evaluations. This makes it especially suited for complex, goal-driven interactions where user experience and conversation quality are paramount.

In a rapidly advancing voice AI landscape, selecting a hybrid solution like Hunar’s not only boosts technical performance but also aligns with long-term business outcomes by offering a scalable, flexible, and future-ready platform that keeps pace with fast innovation.