#Speech

Articles about Speech — exploring patterns, best practices, and real-world implementations in production systems.

8 posts tagged with speech. ← All posts

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Pratik Dhanave · ·6 min read

Building and Productionizing Voice Agents

A voice demo that works in a quiet room with a good headset is a long way from a voice agent that survives a noisy phone call from a real customer. Production voice AI has to handle bad audio, unpredictable humans, failures at every stage, and the peculiar demands of telephony — and it has to be evaluated in ways text systems never require. This closing post is about making a voice agent real.

A voice demo that works in a quiet room with a good headset is a long way from a voice agent that survives a noisy phone call from a real customer. Production voice AI has to handle bad audio, unpredictable humans, failures at every stage, and the peculiar demands of telephony — and be evaluated in ways text systems never require. Making a voice agent real.

#Voice AI#Production#Telephony#AI Engineering#Conversational AI#Speech#Evaluation
Pratik Dhanave · ·6 min read

Speech-to-Speech and the New Realtime Architectures

The cascaded pipeline — ASR, then LLM, then TTS — has powered voice agents for years, but it has an inherent ceiling: every stage adds latency and every conversion loses information. A newer architecture collapses the three models into one that goes from speech directly to speech, promising lower latency and richer understanding. This post compares the cascade with the emerging end-to-end approach, and where each fits.

The cascaded pipeline — ASR, then LLM, then TTS — has powered voice agents for years, but it has an inherent ceiling: every stage adds latency and every conversion loses information. A newer architecture collapses the three models into one that goes from speech directly to speech, promising lower latency and richer understanding. Comparing the cascade with the emerging end-to-end approach.

#Voice AI#AI Architecture#Conversational AI#Machine Learning#Speech#LLMs
Pratik Dhanave · ·6 min read

Turn-Taking, Interruption, and Barge-In

The difference between a voice agent that feels like a conversation and one that feels like a walkie-talkie is turn-taking: knowing when to listen, when to speak, and — hardest of all — gracefully handling being interrupted. Humans do this effortlessly and unconsciously; making a machine do it is one of the subtlest problems in voice AI. This post is about the conversational dynamics that make an agent feel alive.

The difference between a voice agent that feels like a conversation and one that feels like a walkie-talkie is turn-taking: knowing when to listen, when to speak, and — hardest of all — gracefully handling being interrupted. Humans do this effortlessly and unconsciously; making a machine do it is one of the subtlest problems in voice AI. The dynamics that make an agent feel alive.

#Voice AI#Conversational AI#UX#AI Engineering#Speech
Pratik Dhanave · ·6 min read

Latency: The Make-or-Break Constraint

Everything about voice AI comes down to one number: how long the user waits to hear a reply. Get it under the threshold where conversation feels natural and the agent is a delight; miss it and no amount of intelligence saves the experience. This post is about the latency budget — where the milliseconds go, and how streaming the entire pipeline turns an additive delay into something that feels instant.

Everything about voice AI comes down to one number: how long the user waits to hear a reply. Get it under the threshold where conversation feels natural and the agent is a delight; miss it and no intelligence saves the experience. The latency budget — where the milliseconds go, and how streaming the entire pipeline turns an additive delay into something that feels instant. With an interactive turn diagram.

#Voice AI#Latency#Performance#AI Engineering#Conversational AI#Speech#Streaming
Pratik Dhanave · ·5 min read

Text-to-Speech: Giving the Agent a Voice

The last stage of the pipeline is where the agent finally speaks, and it's where an interaction either sounds human or sounds like a robot reading a menu. Modern text-to-speech is remarkably natural, but for a voice agent it must also be fast and streaming — synthesizing speech as the LLM's words arrive, not after — and it must pronounce the messy real world correctly. This post covers TTS for real-time voice.

The last stage of the pipeline is where the agent finally speaks — and where an interaction either sounds human or sounds like a robot reading a menu. Modern TTS is remarkably natural, but for a voice agent it must also be fast and streaming — synthesizing speech as the LLM's words arrive, not after — and pronounce the messy real world correctly. TTS for real-time voice.

#Voice AI#Text-to-Speech#TTS#Speech#Conversational AI#LLMs#Streaming
Pratik Dhanave · ·5 min read

The LLM Turn: The Brain in the Loop

The language model is where a voice agent stops being a transcription toy and becomes something you can actually talk to. But dropping an LLM into a real-time voice loop imposes constraints a chatbot never faces: it must respond in a tight latency budget, produce speech-friendly text, and carry conversation state — all while streaming its answer so the user isn't left waiting. This post is about the LLM stage, adapted for voice.

The language model is where a voice agent stops being a transcription toy and becomes something you can talk to. But dropping an LLM into a real-time voice loop imposes constraints a chatbot never faces: respond in a tight latency budget, produce speech-friendly text, and carry conversation state — all while streaming so the user isn't left waiting. The LLM stage, adapted for voice.

#Voice AI#LLMs#Conversational AI#AI Engineering#Speech#Streaming
Pratik Dhanave · ·5 min read

Speech-to-Text: Hearing the User

The first real stage of a voice agent is turning sound into words, and it's harder than it looks. Modern ASR is astonishingly good on clean audio, but real conversations are full of noise, accents, and disfluencies, and — for a voice agent — the transcript has to arrive fast and incrementally, while the model also figures out when the user has actually stopped talking. This post covers ASR for real-time voice.

The first real stage of a voice agent is turning sound into words, and it's harder than it looks. Modern ASR is astonishingly good on clean audio, but real conversations are full of noise, accents, and disfluencies — and the transcript has to arrive fast and incrementally while the model figures out when the user has actually stopped talking. ASR for real-time voice.

#Voice AI#Speech Recognition#ASR#AI Engineering#Conversational AI#Speech
Pratik Dhanave · ·6 min read

The Anatomy of a Voice Agent

Talking to a computer feels simple — you speak, it answers — but under that simplicity is a real-time cascade of models racing a stopwatch. Audio becomes text, text becomes a response, the response becomes audio, and all of it has to happen fast enough to feel like conversation. This series builds voice AI from the ground up, and it starts with the pipeline that makes a voice agent work.

Talking to a computer feels simple — you speak, it answers — but under that simplicity is a real-time cascade of models racing a stopwatch. Audio becomes text, text becomes a response, the response becomes audio, fast enough to feel like conversation. This series builds voice AI from the ground up, starting with the pipeline that makes a voice agent work — with an interactive diagram.

#Voice AI#Conversational AI#AI Engineering#Speech

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.