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The practical choice is not one model. A responsive in-game conversation normally combines streaming speech recognition, a response language model, and streaming text-to-speech (TTS). Choose each component for your languages, interruption behavior, hardware and measured time to first audible response, then test the complete pipeline while the game is running.
Published timings are useful for narrowing options, but none of the available figures is a controlled benchmark of a named game. Treat model-level and assistant-level numbers as reference points, not promises about gameplay.
Start with the pipeline, not a single checkpoint
A voice turn has at least three model stages:
- Speech-to-text (STT/ASR): captures the microphone stream and produces words.
- Response generation: sends the transcript and game context to a language model (LLM), which emits dialogue tokens.
- Text-to-speech: converts those tokens into audio for the character or companion.
Audio can be streamed between stages. The recognizer may emit partial text, the LLM may generate its first tokens before the full answer exists, and TTS may return audio packets before the sentence is complete. That reduces perceived waiting, but it does not prove that the whole turn is fast enough for a particular game or that a player can interrupt reliably.
Measure the path from the end of a player’s utterance to the first intelligible audio, then measure completion and interruption separately. A fast TTS first packet cannot compensate for a slow recognizer or a long delay before the LLM starts generating.
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Define the target experience before comparing models
Write down the acceptance criteria that matter for your game. They determine which compromises are safe.
- Languages and dialects: include every language the game must recognize and speak. Do not infer accuracy from a model’s name; test game terminology, accents and code-switching.
- Turn style: decide whether players speak short commands, long questions or overlapping dialogue. Streaming ASR is more valuable for long turns, while a command game may prioritize consistent recognition of a small vocabulary.
- Response policy: set a maximum answer length and decide whether the character can begin speaking while the LLM is still generating.
- Interruption: specify whether a new player utterance should stop current audio immediately, finish the current word, or queue the next turn.
- First-audio target: choose a target for time from end-of-speech to the first understandable phoneme. Record full-turn completion separately.
- Hardware envelope: reserve memory and compute for the game, graphics drivers, audio capture and integration code. A model that is fast on an idle machine may stutter when the game is rendering.
- Deployment constraints: confirm that the checkpoint, runtime and voice assets permit your intended distribution and that microphone data stays on the desired devices.
Choose the speech recognizer
Recognition quality and partial-result timing usually dominate the beginning of a turn. Compare language coverage, streaming mode, memory use and robustness to fans, keyboards and game audio.
| Option or evidence | Published detail | How to use the detail |
|---|---|---|
| NVIDIA Parakeet 0.6b CTC English streaming profile | 3.07 GB GPU memory at batch size one in NVIDIA’s ASR NIM support matrix (version 26.02) | This is a profile-specific memory figure, not a universal requirement for every Parakeet runtime. The same NIM requires an NVIDIA GPU with compute capability 8.0 or higher. |
| NVIDIA matrix entries for Parakeet streaming modes and Whisper Large v3 Multilingual | The matrix documents supported models and deployment modes; it does not provide an in-game accuracy comparison. | Use the entries to verify that a deployment path exists for your languages and hardware, then run your own terminology and noise tests. |
| Whisper base in the vox project | On the project’s Linux CUDA build and an RTX 4070 Ti SUPER, 13.8 seconds of audio took 1.1 seconds to process. Its Linux CPU build took 15.1 seconds for the same project test. | These are timings from that project’s implementation and named system. They demonstrate the effect of acceleration, not a guaranteed speed for another Whisper build or game. |
For a game, test partial transcripts rather than only final word error rate. A recognizer that emits an early, slightly revised phrase can let the LLM start sooner, but frequent revisions may trigger wasted generation. Conversely, waiting for a perfectly finalized sentence can make a short exchange feel sluggish.
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Questions to ask during ASR testing
- How long after speech begins does the first partial transcript arrive?
- How often do partial words or names change before finalization?
- Does the model preserve item names, character names and invented terms?
- What happens when the microphone includes music, keyboard clicks or another speaker?
- Can the recognizer stop cleanly when the player starts talking over a character?
Select the response language model independently
The cited material does not identify a single best response LLM for games. Treat generation as a separate choice so you can replace the recognizer or TTS without redesigning dialogue logic.
Evaluate the LLM on the target computer with the same prompt, context window and sampling settings you will ship. Record time to the first generated token, sustained token rate, response length and behavior under contention from the game. A smaller model may begin quickly but lose consistency in character rules; a larger model may produce richer dialogue while delaying speech or consuming memory needed by graphics.
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Constrain outputs for interactive use. A short sentence, explicit stop condition and structured action fields reduce the amount of text that TTS must synthesize. If the character must call game functions, validate those calls before sending spoken text; an unsafe or malformed tool request is a gameplay problem, not a speech-model problem.
Choose a streaming speech generator
TTS determines when a player hears the first response and whether the voice can be cut off naturally. Compare first-packet delay, stream support, language and voice coverage, naturalness at short phrases, and cancellation behavior.
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| Model or paper | Published measurement or capability | Scope and limitation |
|---|---|---|
| Qwen3-TTS-12Hz-0.6B | 97 ms first-packet latency in the Qwen Team technical report dated January 22, 2026 | A model-reported streaming TTS measurement, not a complete voice-turn or gameplay latency. |
| Qwen3-TTS-12Hz-1.7B | 101 ms first-packet latency in the same report | Also a report-specific first-packet result; it does not include ASR, LLM generation or audio-device overhead. |
| VoiceChat-TTS | An August 13, 2026 arXiv preprint describing continuous speech synthesis for interactive agents, including interruption controls | A research preprint. Treat its design as an option to investigate, not as a production benchmark. |
The Qwen Team summarizes the streaming design this way: “Designed for streaming text input and streaming audio output, it achieves a first-packet latency as low as 97 ms (0.6B variant) and 101 ms (1.7B variant).” The quoted values describe the first audio packet under the report’s setup. They do not tell you how quickly a player will hear an intelligible sentence after speaking.
For believable dialogue, test prosody on the exact short fragments your game uses. A voice that sounds excellent on paragraphs may have awkward pauses on one- or two-word responses. Also verify that the runtime can cancel queued audio; otherwise an interrupted character may continue speaking after the player has changed topics.
Interpret latency numbers correctly
“End-to-end latency” should mean one defined interval, not an isolated model metric. A useful trace has these timestamps:
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- Player stops speaking (or the voice-activity detector declares an endpoint).
- First stable transcript appears.
- First response token is generated.
- First audio packet reaches the playback buffer.
- First intelligible phoneme is heard.
- The complete response finishes or is cancelled.
Subtracting the first and last timestamps gives a full-turn result; the interval to the first intelligible phoneme is the responsiveness metric players feel most strongly. Include microphone buffering, resampling, inter-process transport, audio-device buffering and any game-side scheduling in the trace.
| Source and setup | Figure | What it represents |
|---|---|---|
| Local LLM Team guide, April 8, 2026, modern GPU assistant configuration | Estimated total latency of 2–4 seconds | A guide estimate for its described assistant stack, not an independent test and not a gaming measurement. |
| Local LLM Team guide, April 8, 2026, low-tier CPU example | Estimated total latency of 5–8 seconds | Also an assistant estimate tied to that example configuration. |
| Qwen3-TTS-12Hz-0.6B | 97 ms first packet | TTS streaming startup only. |
| Qwen3-TTS-12Hz-1.7B | 101 ms first packet | TTS streaming startup only. |
| vox project, Linux CUDA build on RTX 4070 Ti SUPER | 1.1 seconds to process 13.8 seconds of Whisper-base audio | A project benchmark for one implementation and machine. |
| vox project, Linux CPU build | 15.1 seconds to process the same 13.8 seconds of audio | The corresponding project CPU result, not a general CPU baseline. |
These values cannot be added together to predict a game. They come from different systems and measure different intervals. Use them to identify likely bottlenecks, then collect a single trace from your own integrated build.
Can the full voice pipeline run on CPU only?
Yes, a CPU-only design is possible, but the cited figures show why it may feel slow. The Local LLM Team guide estimates 5–8 seconds for its low-tier CPU assistant example, compared with 2–4 seconds for its modern GPU example. The vox project also recorded a much longer CPU processing time than its CUDA run for the same Whisper-base audio.
Those numbers are not minimum requirements. CPU performance depends on model size, quantization, thread count, thermal limits, runtime and whether the game is using the same cores. A CPU-only stack can be reasonable for turn-based games, push-to-talk commands or low-frequency NPC conversations. For rapid back-and-forth dialogue, measure first-audio delay while the game is rendering; if the target is missed, reduce model sizes, shorten responses, use more aggressive streaming or move selected stages to a supported GPU.
When a GPU is justified
Do not buy a GPU merely because a model page lists one. First profile the chosen stack on the existing PC. Upgrade only when the measured pipeline misses the acceptance target and software changes cannot recover the margin.
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NVIDIA’s ASR NIM has a specific requirement: an NVIDIA GPU with compute capability 8.0 or higher. Its 3.07 GB figure applies to the documented Parakeet 0.6b CTC English batch-size-one streaming profile. Other backends have different requirements and may run partly or entirely on a CPU. The RTX 4070 Ti SUPER in the vox benchmark is a test platform, not a universal recommendation or game minimum.
Account for contention. Reserve memory for the game, textures, driver allocations and audio buffers before assigning the remainder to ASR, LLM and TTS. A model that fits when the game is closed may force paging or reduce frame-time stability when both workloads run together.
A practical selection procedure
- Write the target. Record languages, microphone conditions, first-audio limit, maximum response length, interruption policy and the PC configuration.
- Shortlist ASR options. Confirm language and streaming support in the intended runtime. Include a multilingual option when required, but do not assume the matrix’s listed support equals your game’s accuracy.
- Choose the response model separately. Fix the prompt and sampling settings, then compare first-token delay, sustained generation and adherence to character rules on the target PC.
- Shortlist TTS. Favor streaming input and output when early speech matters. Verify voice, language, cancellation and audio-device compatibility; treat the Qwen first-packet figures as TTS-only references.
- Integrate before judging. Run ASR, LLM and TTS with the game, voice-activity detection, audio playback and context retrieval active. Test warm and cold starts because loading time can dominate the first interaction.
- Log every turn. Capture endpoint time, first transcript, first token, first audio packet, first intelligible audio, completion, cancellation, GPU memory and dropped or duplicated audio.
- Stress the edge cases. Repeat noisy microphones, long turns, rapid interruptions, simultaneous game effects and multiple NPCs. Keep the configuration only if it meets the target without harming frame time.
Design interruption and overlapping speech deliberately
Interruption is a system behavior, not a single model feature. The audio player needs a cancellation command, the TTS stream must stop producing or discard queued packets, the LLM request must be cancellable, and the recognizer must switch to the new speaker turn without mixing old and new text.
Test at least three cases: a player interrupts before the first audio packet, interrupts while a sentence is playing, and starts speaking while the game is emitting loud effects. Measure how much stale audio remains and whether the new response includes text from the cancelled turn. The VoiceChat-TTS preprint is relevant because it studies continuous speech and interruption controls, but its status means you still need an implementation test.
Deployment, privacy and licensing checks
“Local” describes where a particular implementation executes; it does not guarantee identical privacy, licensing or data flow across models. Inspect the checkpoint license, runtime license, voice assets and any telemetry defaults. Confirm that audio is not silently uploaded by a wrapper, that logs do not retain microphone recordings, and that redistribution terms cover the game platform you target.
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Pin model versions and runtime builds for reproducibility. A change in tokenizer, audio sample rate or quantization can alter both quality and latency. Record the exact checkpoint, quantization, device, driver, thread settings and warm-up state with each benchmark.
Decision patterns for common game projects
Multilingual companion
Start with an ASR and TTS pair that explicitly covers the required languages, then verify names and code-switching with recorded game dialogue. Use a modular LLM layer so language or voice changes do not force a complete rewrite.
Push-to-talk tactical commands
Prioritize fast endpoint detection, reliable command transcription and short, constrained responses. A CPU-only stack may be acceptable if your measured first-audio and command-confirmation times meet the design target.
Free-form NPC conversation
Test response consistency and cancellation as heavily as raw token speed. Limit answer length, stream text into TTS and keep a fallback line ready when a model is still generating.
GPU-constrained laptop
Benchmark with the game running, reduce model sizes or quantize before shopping for hardware, and watch memory pressure. If a service such as NVIDIA ASR NIM is selected, verify its documented compute-capability requirement rather than assuming any NVIDIA card qualifies.
Troubleshooting slow or broken turns
- First transcript is late: inspect voice-activity endpoint time, microphone buffering and ASR startup. Keep the recognizer warm and test shorter endpoint silence thresholds without cutting off natural pauses.
- Transcript is quick but speech starts late: profile LLM first-token time and prompt length. Stream partial text to TTS only when revisions can be handled safely.
- First packet is fast but words are delayed: check audio-device and playback-buffer latency, resampling and packet accumulation in the integration layer.
- Game frame time spikes: inspect GPU memory and CPU contention while rendering. Move a stage to another device, lower concurrency or use a smaller model before increasing batch size.
- Interrupted voices continue: implement cancellation through the LLM request, TTS stream and playback queue; dropping only the final audio buffer leaves stale packets audible.
- CPU mode feels unusable: compare the measured trace with the 5–8-second CPU estimate from the Local LLM Team guide, then reduce model size or response length and retest rather than treating that estimate as a hard limit.
What the available evidence can and cannot establish
The published material supports a component-by-component comparison and a disciplined measurement method. It does not provide a controlled benchmark of any named game with simultaneous rendering, nor does it identify a universal winning combination of ASR, LLM and TTS. Select the stack that meets your own first-audio, quality and interruption targets on the intended PC, and describe every timing with its model, runtime, hardware and measurement scope.
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