In our previous blog about Root Causes of Latency in AI Voice Systems, we diagnosed that latency originates in each step of voice interaction process: audio capture, recognition, language processing, response generation, and speech synthesis: and builds up in output. The total amount of delay defines whether the conversation is unnatural, flat and robotic or human-like.
It’s obvious that only knowing why latency occurs does not make Voice AI systems responsive, teams have to take practical steps to improve the overall system. Most teams take initial steps to reduce latency by optimizing their existing system through techniques like tuning buffers, adjusting chunk sizes, improving model speed, trimming context, and shortening response paths. These techniques do not change the fundamental structure of the system but can reduce waiting time and improve conversational flow when applied correctly.
In this blog, we will explore why it is necessary to reduce latency as a business or brand and what are the techniques that teams use for reducing latency via optimization.
Business Impact of High Latency in Voice AI
The speed at which AI voice agents respond is not just a technical detail rather it directly influence your brand and business by affecting following parameters:
- Brand Perception
- Average Handle Time (AHT)
- Customer Satisfaction Score (CSAT)
- Call Abandonment Rate
- Sales Conversions and Revenue
Brand Perception
When businesses run outbound or cold calling campaigns, AI voice agents are the first point of contact for customers. Their responsiveness and performance directly impact how prospect perceives a business or brand. If AI assistants are slow, prospects will have frustrating experiences and negative perception.
Average Handle Time (AHT)
Latency increases the overall duration of time. For example a call that must end in 60 seconds might take 70 seconds to complete due to silences. Therefore, a call center with slow AI assistants handles comparatively less interaction as compared to fast-responding AI voice agents.
Customer Satisfaction Score (CSAT)
When customers experience unwanted pauses and silences during calls, they may feel unheard or wonder if the call has been disconnected. This experience lowers the satisfaction level.
Call Abandonment Rate:
When a voice agent’s response is delayed, the caller may cut the call before response, leading to a higher call abandonment rate.. In this way, problem and issues remain unsolved and outbound calls may remain ineffective and might not able to convert cold leads into warm and hot leads
Sales Conversions and Revenue
In sales and lead qualification scenarios, even a few seconds of silence can cause a significant percentage of inbound callers to abandon the call and lose interest. That naturally leads to lost prospects, sales, and revenue.
So, these impacts show how your business grows and how it is perceived by the prospects and customers show how directly and indirectly depends on your brand’s AI voice assistant. Therefore, leaving them with latency is a risk for business survival. So, it’s important to solve this problem.
Deciding the Right Latency Metrics for Voice AI Optimization
In our previous blog, we learned that in the end-to-end pipeline, latency appears in every step and has an additive nature that becomes noticeable and appears as delay between when the speaker stops talking and the AI assistant starts responding.
In the optimization process, it’s very important to measure latency precisely and meaningfully. Without the right metrics, the fixes feel random and ineffective.
Suppose a delay of 330 ms appears during the speech recognition phase of the speech interaction pipeline while only 220ms appears in language understanding and intent detection by LLM models. So it’s better to address the speech recognition delay because it will produce a larger improvement in end-to-end latency rather than fixing the one introduced by LLM models.
This is why it’s very important to define the right metric to optimize the pipeline because accurate measurement allows engineers to identify dominant bottlenecks and prioritize fixes that reduce the highest sources of delay with minimal changes across the pipeline.
STT Time-to-First-Byte (TTFB)
It measures the time between the initiation of users speech and when the first partial transcript is received from speech recognition system to LLM model for further processes.
Partial transcripts allow downstream stages like intent detection or response planning to begin earlier. STT Time-to-First-Byte shows how quickly the system becomes responsive and interactive..
LLM Time-to-First-Token (TTFT)
It measures the time from when the text input to the language model is submitted until the first output token is generated.
LLM Time-to-First-Token shows how quickly the model starts responding. Practically, a low TTFT is important because downstream text-to-speech can begin working sooner, and partial responses can be used to trigger audio synthesis.
TTS Time-to-First-Byte (TTFB) + “Finish Latency”
It measures how quickly the first byte of synthesized audio starts playing and how long it takes for the full response audio to generate and play.
The point when the user hears the first sound is what makes the system feel fast or slow. Hearing sound quickly gives the impression that the system is responding. It’s important to measure both when audio starts and when it ends because the start time shows how responsive the system is, while the completion time shows how efficiently the speech is generated and played.
End-to-End Latency
It measures the total time from when the user begins speaking to when they first hear the AI’s response.
End-to-end latency shows how real conversations feel like. It represents cumulative delays across capture, recognition, understanding, generation, and synthesis. For high-performance conversational AI agents, usually latency under 1000ms (around ~300–800 ms) is considered OK for smooth conversation. While 2000ms is considered an upper limit before responses start to feel disruptive.
How to Reduce Latency in Voice AI System
In this section, we will see how we can reduce the latency we diagnosed to appear in different stages of the voice interaction process.
Reducing Audio Capture & Streaming Delays
It is the first stage where latency enters a Voice AI system. Every downstream phase depends on input provided by this phase, even small delays at this stage propagate through the entire pipeline. If audio capture is slow, all later stages have to wait.
Delay can be reduced via:
- Tuning Buffer Size
- Tuning Chunking Strategy
- Managing Jitter Buffering
Tune Buffer Size
A buffer is a temporary storage area where incoming audio is stored before processing begins. The size of this buffer directly affects how quickly processing can begin.
To reduce latency, buffers should be small enough to fill quickly but extremely small buffers increase processing frequency overhead.
Tune Chunking Strategy
Chunking strategy controls how buffered audio is split into pieces and processed.
The chunk size directly influences when the components of the voice interaction system begins processing. Too small chunk size increases size coordination effort because downstream components must handle more frequent updates. And too large chunk size forces downstream stages to start late and increase the idle time.
Google recommends a ~100 ms chunk size as a practical trade-off between latency and efficiency. This size allows the speech recognizer to begin processing promptly. But for best results, measure latency and efficiency with 20, 50, 80, 100 ms to find the one that suits best.
Manage Jitter Buffering
During the voice interaction, audio data is sent in the form of packets. These packets do not arrive at the same time due to many reasons like network congestion, different routing paths, processing delays, etc. As a result of this variability in time, audio may play in broken parts.
To overcome this, engineers add network jitters to hold the packets for some time and then release them in accurate order to maintain smoothness in audio. Size of this jitter buffer can add latency and affect quality of audio; too small jitter may not be able to maintain smoothness whereas, too large may add unnecessary delay before reply. Both situations reduce audio quality and harm performance.
For a system with stable connectivity, a static buffer in the range of 30 ms to 50 ms is enough for smooth audio without adding excessive delay. Whereas, systems with more variable network conditions, adaptive jitter buffers are suitable. It can expand up to 100 ms to 200 ms to maintain continuous audio flow.
Reducing Speech Recognition (ASR) Latency
Speech recognition This process often adds a significant latency because it’s one of the most computationally intensive steps because ASR has to analyze continuous audio, extract its features, and convert sound into text. Here delays can be reduced by:
- Preferring Streaming ASR Over Batch ASR
- Using Partial Transcripts Carefully
- Reducing ASR Compute Cost
- Prefetching and Hypothesis Prefetch
Prefer Streaming ASR Over Batch ASR
At the ASR stage, latency can be further reduced by preferring streaming AR over Batch ASR. In Batch ASR, the transcriber waits for the whole speech to finish and then starts processing it into text. In this way, the whole system sits idle until the speech is over. Whereas in streaming ASR, the audio is transcribed incrementally as speech arrives instead of waiting for speech to finish. This reduces the idle time by starting off the downstream processes earlier contributing to reduction in latency.
Use Partial Transcripts Carefully
In streaming ASR, partial transcripts are generated while the user is still speaking. Intent detection and other downstream components can see these partial transcripts, but they are not used to trigger direct actions. Instead, downstream processes treat them as early signals to begin preparation, such as setting up possible response templates or narrowing down intent candidates.
Using partial transcripts in this careful way allows downstream stages to complete some of their work earlier, which reduces overall latency. Systems that act prematurely on partial text risk incorrect responses, while systems that ignore partial output miss the opportunity to reduce waiting time.
Reduce ASR Compute Cost When Needed
Acoustic model complexity directly affects ASR latency. Using too complex and detailed models can increase the transcription accuracy but costs time. Therefore, using a model that is optimized for real-time inference and avoiding too complex models for conversational use is key to achieve the right balance between speed and accuracy.
Prefetching and Hypothesis Prefetch
Voice assistant systems show that early speech recognition hypotheses often remain consistent with final results. This opens the door to prefetching, where the system begins preparing likely responses based on early ASR output.
Latency can be reduced by caching preliminary interpretations or retrieving potential responses in advance. If the hypothesis later changes, the prefetched work can be discarded or updated. When implemented as caching rather than execution, this technique effectively hides latency instead of removing it.
Because the system still follows the same execution order, prefetching is considered a latency reduction technique, not an architectural redesign.
Reducing Language and Intent Processing Latency
In the language and intent processing stage, delay builds up more especially as conversations grow longer. Unlike speech recognition delays, this latency is less visible but can accumulate over time, slowing down responses without obvious warning signs.
Delay can be reduced by
- Reducing Prompt and Context Construction Overhead
- Controlling Context Window Growth
- Improving Time-to-First-Token (TTFT)
Reduce Prompt and Context Construction Overhead
The transcribed text cannot be interpreted in vacuum; rather, the system needs context in form of previous dialogue turns, conversation state, or system instructions to comprehend it.
This step takes time because the system might have to gather related information from multiple sources like knowledge base, CRM, and other related information sources. Even if the inference time is less, context construction introduces significant additional delay.
Latency can be reduced by caching system instructions and prompts that do not change during a conversation, using predefined templates instead of regenerating them repeatedly and eliminating redundant formatting and boilerplate text.
Control Context Window Growth
Reducing latency here means keeping only the most relevant dialogue turns, summarizing older conversation content, and avoiding repeated injection of the same history. This approach reduces the amount of input the model must process while preserving essential context. It is a “less input” optimization that improves speed without introducing concurrency or changing system structure.
Latency can be reduced by controlling the context window. This can be done by only keeping the relevant dialogues, summarizing older conversation content, and avoiding repeated injection of the same history. These approaches reduce inputs which in turn increases speed and reduces latency.
Improve Time-to-First-Token (TTFT)
It measures the time from when the text input to the language model is submitted until the first output token is generated.
Lower TTFT allows response generation to begin earlier and enables downstream processes such as text-to-speech preparation to start sooner. Optimizing TTFT improves the system’s responsiveness. TTFT can be optimized by
- Reduce the amount of text sent to the model because the more text the model has to read, the longer it takes before it can start responding.
- Avoid rebuilding prompts every turn and instead keep fixed instructions cached, reuse templates, and add only the new user message
- Use simpler response styles by avoiding complex instructions like that cause the model to plan longer responses before starting.
- Start generation as soon as possible by allowing the model to begin working on output early and leave refinement as more tokens follow
Reducing Response Generation Delays
Response generation delays include additional time that comes in the process when creating the content of the reply and controlling when that reply should be delivered.
Response generation latency can be reduced in the following ways:
- by keeping answers concise,
- starting speech immediately when the first part of the response is ready, and
- tuning turn-taking logic so the system talks naturally without awkward pauses and too quick responses.
FAQs About Reducing Latency in Voice AI Systems
1. Why is reducing latency critical for voice AI systems?
It is important to reduce latency because AI voices with unnatural pauses and delays feel robotic. The main goal of voice AI systems is to make the conversation feel human-like. So, high latency makes everything appear flat and unnatural. Moreover, when used for outbound and cold calling, voice AI systems affect brand perception, average handle time (AHT), customer satisfaction score (CSAT), call abandonment rate, and sales conversions and revenue.
2. What are the most important latency metrics to track in voice AI optimization?
Following are the most important latency metrics:
- STT Time-to-First-Byte (TTFB)
- LLM Time-to-First-Token (TTFT)
- TTS Time-to-First-Byte
- End-to-end latency.
3. How does audio capture and streaming affect overall voice AI latency?
Audio capture and streaming are the initial sources of latency. It occurs when audio enters the system and is processed for downstream steps. Here latency can occur due to three main reason:
- Buffer Sizing
- Chunking strategies
- blocking vs streaming capture
