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ARECHO: Autoregressive Evaluation via Chain-Based Hypothesis Optimization for Speech Multi-Metric Estimation
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Proposes ARECHO, an autoregressive speech multi-metric evaluator that tokenizes numerical and categorical metrics, predicts them via a dynamic classifier chain, and uses two-step confidence-oriented decoding, outperforming UniVERSA on speech quality metrics.
Problem
Joint estimation of many heterogeneous speech metrics (quality, intelligibility, MOS, categorical attributes) is hard due to differing scales, partial labels, and independent parallel prediction that ignores inter-metric dependencies.
Approach
Tokenize all metric values into a shared discrete vocabulary (percentile-based bins for numerical, direct mapping for categorical) and model metrics as an interleaved metadata/value token sequence in a randomly permuted dynamic classifier chain, decoded with a two-step confidence-driven beam search.
Key results
- On the Base configuration average test set, ARECHO reaches MSE 23.26, LCC 0.86, KTAU 0.72 versus UniVERSA's 96.99, 0.69, 0.52.
- Classification accuracy on the Base average test set improves from 0.69 (UniVERSA) to 0.74 (ARECHO), with F1 0.57 vs 0.45.
- Authors report computing the same metrics with original LLM-based evaluators is over 100x slower than a single ARECHO forward pass.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add ARECHO's dynamic classifier chain to uniform-tokenized numerical prediction | UniVERSA-T · Avg. Test (Base) | MSE: 37.72 -> 23.26 | Helped |
| Add ARECHO's dynamic classifier chain to uniform-tokenized numerical prediction | UniVERSA-T · Avg. Test (Base) | LCC: 0.79 -> 0.86 | Helped |
| Add ARECHO's dynamic classifier chain to uniform-tokenized numerical prediction | UniVERSA-T · Avg. Test (Base) | F1: 0.49 -> 0.57 | Helped |
| Add ARECHO's dynamic classifier chain to uniform-tokenized numerical prediction | UniVERSA-T · Avg. Test (Scale) | MSE: 39.32 -> 25.21 | Helped |
| Add ARECHO's dynamic classifier chain to uniform-tokenized numerical prediction | UniVERSA-T · Avg. Test (Scale) | LCC: 0.86 -> 0.85 | Hurt |
| Tokenize numerical metrics using uniform quantization (ablation) | UniVERSA · Avg. Test (Base) | MSE: 96.99 -> 37.72 | Helped |
| Use percentile-based rather than uniform-over-value binning (ablation) | ARECHO tokenization · Synthesized | RMSE: 0.100 -> 0.056 | Helped |
| Use percentile-based rather than uniform-over-value binning (ablation) | ARECHO tokenization · Enhanced | RMSE: 0.223 -> 0.139 | Helped |
| Use percentile-based rather than uniform-over-value binning (ablation) | ARECHO tokenization · Corrupted | RMSE: 0.512 -> 0.261 | Helped |
| Increase percentile-based quantization from 500 to 1000 bins (ablation) | ARECHO tokenization · Synthesized | RMSE: 0.056 -> 0.034 | Helped |
| Increase percentile-based quantization from 500 to 1000 bins (ablation) | ARECHO tokenization · Enhanced | RMSE: 0.139 -> 0.103 | Helped |
| Increase percentile-based quantization from 500 to 1000 bins (ablation) | ARECHO tokenization · Corrupted | RMSE: 0.261 -> 0.165 | Helped |
Surprising findings
- UniVERSA retains the highest LCC under the Scale configuration, indicating tokenization can lose granularity for some regression tasks.
- Under the Scale configuration, ARECHO gains are smaller on synthesized and enhanced speech, with UniVERSA achieving higher MSE on synthesized speech.
Limitations
- Tokenization may introduce granularity loss for some numerical metrics, trading off against precision.
- Performance gains are smaller on synthesized and enhanced speech under the imbalanced Scale training set.
Experimental setup
Datasets: OWSM-V3 subset URGENT2024 simulated training data Voice Bank+DEMAND URGENT2024 enhanced speech VoiceMOS 2022 NISQA
Benchmarks: URGENT2024 blind test set VoiceMOS 2022 NISQA Voice Bank+DEMAND
Metrics: MSE LCC KTAU ACC F1
Baselines: UniVERSA UniVERSA-T
Builds on: UniVERSA classifier chain WavLM VERSA
Closest papers by meaning
- SCOREQ: Speech Quality Assessment with Contrastive Regression (NeurIPS 2024 · similarity 0.86)
- Audio Large Language Models Can Be Descriptive Speech Quality Evaluators (ICLR 2025 · similarity 0.86)
- HAINAN: Fast and Accurate Transducer for Hybrid-Autoregressive ASR (ICLR 2025 · similarity 0.84)
- Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation (NeurIPS 2025 · similarity 0.84)
- Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis (ICLR 2025 · similarity 0.83)
- BIGOS V2 Benchmark for Polish ASR: Curated Datasets and Tools for Reproducible Evaluation (NeurIPS 2024 · similarity 0.83)
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