# Lts Voiceagent Eval

> This evaluation probes the accuracy-latency-efficiency trade-off of streaming voice agents under realistic ASR conditions. It measures how well a system maintains reasoning quality while minimizing computational overhead and response delays when processing natural speech with disfluencies, misrecognitions, and non-uniform speaking rates. Use when the user wants to benchmark on VERA (AIME and GPQA-Diamond), Spoken-MQA, BigBenchAudio, Pause-and-Repair Benchmark, or asks about evaluating this task. Reports Accuracy.

- Skill: `qhjqhj00/lts-voiceagent-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/lts-voiceagent-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/lts-voiceagent-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/lts-voiceagent-eval

---


# lts-voiceagent-eval

> LTS-VoiceAgent: A Listen-Think-Speak Framework for Efficient Streaming Voice Interaction via Semantic Triggering and Incremental Reasoning — Wenhao Zou et al. (arXiv:2601.19952, 2026)

## What this evaluates

This evaluation probes the accuracy-latency-efficiency trade-off of streaming voice agents under realistic ASR conditions. It measures how well a system maintains reasoning quality while minimizing computational overhead and response delays when processing natural speech with disfluencies, misrecognitions, and non-uniform speaking rates.

## Datasets

- **VERA (AIME and GPQA-Diamond)** — total ?; splits: test (-1)
- **Spoken-MQA** — total ?; splits: test (-1)
- **BigBenchAudio** — total ?; splits: test (-1)
- **Pause-and-Repair Benchmark** — total ?; splits: test (-1)

## Metrics

- `Accuracy` **(primary)** — range: percent
  - Standard exact-match or pass@1 accuracy on question-answering tasks, measuring the percentage of correctly answered queries.
- `Time-to-First-Sentence (TTFS)` — range: other
  - Wall-clock time from the start of audio input to the generation of the first complete sentence.
- `Number of Forward-pass Evaluations (NFE)` — range: other
  - Total count of LLM forward-pass executions performed during the inference process.
- `Number of Interruption Times (NIT)` — range: other
  - Count of mid-reasoning interruptions triggered by subsequent user speech or ASR revisions.
- `Interruption Rate` — range: percent
  - NIT divided by NFE, representing the fraction of forward passes that are interrupted by subsequent speech.

## Input / output format

**Input**: Streaming audio processed by a unified internal ASR API at 200ms granularity (16kHz mono PCM, 3,200 samples per chunk), yielding incremental text transcripts fed to the LLM.

**Output**: Streaming text response generated via greedy decoding (temperature=0, max 4,096 new tokens) from the Qwen3-8B backbone.

## Scoring recipe

```python
def compute_metrics(predictions, golds, nfe, nit):
    correct = sum(1 for pred, gold in zip(predictions, golds) if pred.strip() == gold.strip())
    accuracy = (correct / len(golds)) * 100
    interruption_rate = (nit / nfe) * 100
    return {'accuracy': accuracy, 'interruption_rate': interruption_rate}
# TTFS and NFE are recorded directly from system logs during inference
```

## Common pitfalls

- Evaluating the semantic trigger classifier in isolation rather than end-to-end, which fails to capture ASR jitter and the non-unique nature of spoken semantic boundaries.
- Comparing against baselines that use fixed text chunks or heuristic noise instead of realistic streaming ASR, leading to inflated efficiency claims.
- Confusing Time to First Token (TTFT) with Time-to-First-Sentence (TTFS), which better reflects perceived responsiveness in interactive voice agents.

## Evidence (verbatim from paper)

> We evaluate quality via Accuracy, latency via Time to First Token and Time-to-First-Sentence (TTFS), and efficiency via the Number of Forward-pass Evaluations (NFE) and the Number of Interruption Times (NIT). To quantify trigger validity, we report the Interruption Rate, defined as $	ext{NIT}/	ext{NFE}$; lower values indicate fewer mid-reasoning interruptions caused by subsequent speech and therefore more precise trigger timing.

## Citation

```bibtex
@misc{zou2026ltsvoiceagent,
  title={LTS-VoiceAgent: A Listen-Think-Speak Framework for Efficient Streaming Voice Interaction via Semantic Triggering and Incremental Reasoning},
  author={Wenhao Zou et al.},
  year={2026},
  note={arXiv:2601.19952}
}
```

- arXiv: 2601.19952