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Joint Analysis of Latent Dimensionality and Frame Rate in Continuous Audio Encoders
Kyudan Jung, Sehyun Lee, Son-ha Jo, Jaegul Choo, Sanghyuk Shoi
TL;DR
The joint effects of latent width and frame rate on recognition with continuous audio representations remain unclear. The paper trains sixteen codecs across both dimensions and evaluates downstream tasks and PCA interventions. Results show width–rate interactions in downstream performance and distinct reconstruction and ASR sensitivities to component removal.
Problem
Prior work varied width and frame rate separately, leaving their joint effect on recognition with continuous representations unclear.
Source: §1 · p. 1Method
The study trains sixteen codecs across four widths and four frame rates under a shared recipe, evaluates downstream tasks, and probes latent directions with frozen-model PCA interventions.
Source: §1 · p. 1, §2.2 · p. 2Results
Across the grid, ASR and SQA favor moderate widths at higher rates and larger widths as rates fall; PCA preserves 1024-dimensional ASR but severely degrades high-rate 512-dimensional ASR despite a small reconstruction penalty.
Source: §1 · p. 1, §5 · p. 4, §4.4 · p. 4Takeaways & Limitations
The results support jointly selecting width and rate for each downstream task while considering representation organization, not reconstruction quality alone.
Source: §5 · p. 4Reconstruction tests use cropped inputs and aggregate acoustic metrics, so they cannot rule out loss of specific linguistic cues.
Source: §4.4 · p. 4
Abstract
from arXiv · showhide
Continuous audio encoders compress audio along feature and time axes through latent width and frame rate, but their joint effect on downstream performance remains unclear. We train sixteen encoders spanning four widths and four frame rates, with downstream adapters and probes, using matched training protocols. Despite generally improved reconstruction at larger widths, automatic speech recognition (ASR) and spoken question answering (SQA) favor moderate widths at higher rates, with the best observed widths shifting toward larger values under stronger temporal compression. Frozen-model PCA interventions reveal distinct reconstruction and recognition sensitivities: removing the trailing half of the components substantially degrades ASR in high-rate 512-dimensional encoders with comparatively small reconstruction penalties, whereas 1024-dimensional encoders largely preserve both. Yet the projected 1024-dimensional model underperforms unmodified narrower models on ASR at 12.5Hz. These findings identify a width--rate interaction in downstream utility and suggest that how representations are organized during training matters beyond reconstruction fidelity and compressibility.
1. INTRODUCTION
Prior work varied codec width and frame rate separately, leaving their joint effects on recognition with continuous representations unclear. This paper tests all sixteen width–rate combinations and finds task-dependent optima, with PCA interventions revealing different reconstruction and ASR sensitivities.
1. INTRODUCTION
- Prior work varied width and frame rate one at a time, leaving their joint effect on recognition with continuous representations unclear.Earlier studies examined reconstruction, TTS generation, or frame-rate selection for quantized speech QA.Source: §1 · p. 1
- The study trains sixteen width–rate combinations with a common backbone and recipe, evaluating reconstruction, ASR, SQA, and speaker identification.The grid uses four widths and four frame rates.Source: §1 · p. 1
- PCA interventions show that 1024-dimensional encoders largely preserve reconstruction and ASR after half the components are removed, but projected models still trail unmodified narrower models on ASR at 12.5 Hz.The analysis motivates the distinction between removing directions after training and learning with a narrower bottleneck.Source: §1 · p. 1
- ASR and SQA favor moderate widths at higher frame rates and larger widths as frame rate decreases.The best observed ASR and SQA widths shift upward under stronger temporal compression.Source: §1 · p. 1
2. METHODOLOGY
The study varies bottleneck width and frame rate in DAC-VAE-based codecs while retaining a shared training objective, dataset mix, and recipe across the grid.
2.1. Architecture
- The sixteen codecs vary latent width and target frame rate, using learned stride-2 downsamplers and corresponding upsamplers for rates below 25 Hz.The decoder receives 25-Hz features, while each encoder is trained at its target-rate bottleneck rather than compressed post hoc.Source: §2.1 · p. 2
2.2. Training Objective and Dataset
- All codec settings use the same weighted objective, combining mel-spectrogram, multi-scale STFT, KL, adversarial, and feature-matching losses.The stated weights are 100, 1, 10^-3, 1, and 2, respectively.Source: §2.2 · p. 2
- Every model uses the same mixed speech, general-sound, and music data and is trained for 50 epochs with a shared optimization recipe.The data sources are LibriSpeech, WavCaps, and MAESTRO; training uses AdamW, learning rate 3 × 10^-5, batch size 8 per worker, and 3,000 steps per epoch.Source: §2.2 · p. 2
3. EXPERIMENTS
The experiments evaluate reconstruction and three downstream tasks across sixteen codecs, then use PCA interventions to examine latent directions. Reconstruction is measured with PESQ and SI-SDR, while downstream evaluation uses task-specific adapters and probes.
3. EXPERIMENTS
- The experiment suite compares reconstruction and downstream performance across sixteen DAC-VAE-based codecs and uses PCA interventions to examine reliance on latent directions.Source: §3 · p. 2
3.1. Reconstruction
- Reconstruction is evaluated on fifty 5.12-second LibriSpeech test segments using PESQ at 16 kHz and SI-SDR at 48 kHz.PESQ assesses perceptual speech quality, while SI-SDR measures waveform fidelity.Source: §3.1 · p. 2, Table 1 · p. 2
3.2. Downstream Tasks
- ASR, SQA, and speaker identification use task-specific adapters or probes, with ASR measured by WER and SQA by Cover-EM.ASR and SQA use a trainable adapter to frozen codec representations and a frozen language model; speaker identification uses statistics pooling and a linear softmax probe.Source: §3.2 · p. 2
4. RESULTS AND ANALYSIS
Across the width–rate grid, downstream preferences differ from reconstruction optima; PCA interventions further distinguish reconstruction tolerance from ASR sensitivity and show that post-hoc dimension removal differs from training with a narrower bottleneck.
4.2. Useful width depends on temporal rate
- Reducing the rate from 25 to 12.5 Hz changes WER only slightly at 128–512d, while SQA and speaker identification generally improve.At 12.5 Hz, the adapter processes half as many audio tokens and its audio self-attention score matrix has approximately one quarter as many entries.Source: §4.2 · p. 3
- At 12.5 Hz, all four downstream metrics reach their best observed scores, while ASR and SQA favor wider latents as frame rates fall.At 12.5 Hz, 1024d gives the best reconstruction but the weakest downstream results; the width rankings are point estimates, not statistically established unique optima.Source: §4.2 · p. 3
4.3. Latent information analysis with PCA
- After retaining the leading half of PCA components, 1024-dimensional models largely preserve ASR performance, while narrower models generally show greater sensitivity.PCA ranks explained variance rather than task relevance, and robustness to component removal does not establish information density or absolute accuracy.Source: §4.3 · p. 3, Table 3 · p. 3
4.4. Codec decoding after PCA
- At every tested frame rate, the mean reconstruction penalty from top-half PCA removal decreases with width, but high-rate 512d ASR is especially unstable despite a smaller reconstruction penalty than 128d or 256d.The reconstruction test uses cropped inputs and aggregate acoustic metrics, so it cannot rule out loss of specific linguistic cues.Source: §4.4 · p. 4, Table 4 · p. 4
4.5. Bottlenecks and Representation Learning
- At 1024d, top-half PCA largely preserves reconstruction and ASR, yet ASR remains below unmodified narrower models at 12.5 Hz, distinguishing post-hoc removal from training with a narrower bottleneck.The compression-pressure mechanism still requires controlled training experiments.Source: §4.5 · p. 4
5. CONCLUSION
Across sixteen encoders, reconstruction peaks at the widest, highest-rate setting, whereas none of the downstream measures does. The PCA results show that representation organization during training, not simply post-hoc dimension removal, matters to downstream performance.
5. CONCLUSION
- Across sixteen encoders, reconstruction peaks at the widest, highest-rate corner of the grid, but none of the four downstream measures does.All four downstream measures peak at 12.5 Hz with 256–512 dimensions, where ASR matches its 25 Hz accuracy and the other three improve.Source: §5 · p. 4
- Top-half PCA removal leaves 1024-dimensional encoders nearly unchanged in reconstruction and ASR, but severely degrades ASR in high-rate 512-dimensional encoders despite a small reconstruction penalty.The contrast separates decoder tolerance from recognizer sensitivity under the intervention.Source: §5 · p. 4
- The projected 1024-dimensional model still trails unmodified narrower models at 12.5 Hz, indicating that post-hoc removal differs from training under a narrower bottleneck.The conclusion is that width and rate jointly shape what latents preserve and make accessible.Source: §5 · p. 4