Probing Neural Audio Codecs with Speaker Traits and Speech Characteristics

1 Introduction

• Neural audio codecs support low-bitrate speech coding and speech-language-model tokenization, while residual vector quantization progressively quantizes residuals to support multiple bitrates.Some recent codecs assign a distinct role to the first codebook through teacher distillation.Source: §1 · p. 1
• The study targets an open question: where specific speaker traits and speech characteristics are preserved in codec hierarchies, beyond conventional linguistic-content, speaker-similarity, and perceptual-quality evaluations.These traits matter for responsive spoken-dialogue systems, accessible speech technologies, and equitable performance across speakers.Source: §1 · p. 1
• The paper asks at what depth each trait is preserved, whether classes within a trait share depths, and how codec design choices relate to trait depth.Source: §1 · p. 1
• Across 17 codecs, machine listeners compare predictions on original speech with predictions on reconstructions from cumulative codebook prefixes; trait depth is where reweighted Cohen’s κ first reaches 90% of its full-depth value.Trait depth is used as a proxy for how a trait is encoded and recoverable in the hierarchy.Source: §1 · pp. 1–2
• Sex has the shallowest trait depth, followed by speech flow; voice quality and age group require deeper stacks, with other traits in between.Speech-flow classes have relatively similar depths, while non-verbal sounds vary more; laughter is preserved earlier than sneezing.Source: §1 · p. 2

2.1 Neural Audio Codecs

• Neural audio codecs use residual vector quantization to quantize successive residuals, while other designs use multiple temporal resolutions, a single codebook, or finite scalar quantization.Teacher-distillation approaches assign early-codebook roles using speech representations, self-supervised features, phonemes, or ASR features.Source: §2.1 · p. 2

2.2 Probing Neural Audio Codecs

• Prior accounts describe coarse-to-fine codebook stacks and proposed allocations of linguistic versus paralinguistic information, but do not locate specific traits; evaluations remain limited in traits and codecs.Prior work probes selected traits or codec representations, leaving a systematic account across diverse traits and hierarchies lacking.Source: §2.2 · p. 2

2.3 Speaker Traits and Speech Characteristics

• Trait predictors cover constructs including sex, age, accent, voice quality, emotion, speech flow, and non-verbal sounds; the protocol compares listener predictions on original and partial reconstructions.VoxProfile is used for most traits, Voxlect for accent, and SSLAM for non-verbal sounds; Figure 1 outlines the partial-codebook and agreement procedure.Source: §2.3 · pp. 2–3, Figure 1 · p. 3

3.1 Probing Protocol

• The study probes 17 publicly available multi-codebook codecs, spanning 12.5–75 Hz framerates, 16 or 24 kHz sampling rates, and 4–32 codebooks.The codec set includes models with and without teacher distillation and streaming.Source: §3.1 · p. 3
• Each utterance is reconstructed from the first k codebooks, and machine listeners predict seven traits on both the reconstruction and original audio.Voxlect and SSLAM represent accent and non-verbal sounds, respectively; VoxProfile represents the remaining traits. Auxiliary listeners’ agreement scores strongly align with representative listeners (Spearman’s ρ = 0.87–0.98).Source: §3.1 · p. 3
• Trait depth is the relative codebook depth where reweighted Cohen’s κ first reaches 90% of its full-depth value.Reweighting is intended to mitigate bias from class prevalence, class balance, and differences in listener accuracy; weights remain fixed across codecs and depths.Source: §3.1 · p. 4

3.2 Datasets

• Evaluation uses trait-specific datasets, including VoxProfile test sets for sex and age, Voxlect’s English test set for accent, and VocalSound’s test split for non-verbal sounds.Voice quality uses ParaSpeechCaps, emotion uses a randomly selected 10% of IEMOCAP, and speech flow uses random 20% samples from SEP-28k and FluencyBank test sets.Source: §3.2 · pp. 4–5

4.1 Across-Trait Analysis

• Sex has the shallowest trait depth, followed by speech flow; voice quality and age group are deeper, with emotion, accent, and non-verbal sounds between them.Figure 2 shows each codec's depth and the mean across codecs; the authors offer temporal cue scales and articulatory variation as possible interpretations, requiring further investigation.Source: §4.1 · pp. 5–6, Figure 2 · p. 6

4.2 Within-Trait Analysis

• For most traits, classes have different trait depths, but speech-flow classes are more similar, with fluency and disfluency types requiring approximately 30–40% relative codebook depth to reach 90% of full-depth κ.All age groups tend to show broadly similar trait depths; senior adults over 60 tend to be shallower, followed by young adults aged 18–30, while adults aged 30–60 are slightly deeper.Source: §4.2 · pp. 6–8
• Voice-quality classes vary by dimension, and laughter reaches its 90% threshold at 20% depth, compared with 50–60% for sneezing or coughing.Timbral qualities tend toward middle depths; deep and shrill voices and singsong and pitchy styles tend shallower, while staccato and flowing timing attributes tend deeper; clarity and volume are mixed.Source: §4.2 · p. 7

4.3 Codec Design

• Matched codec comparisons associate higher framerates with earlier trait information, while distillation improves first-codebook preservation but still requires deeper stacks to reach 90%.The aggregate high-framerate comparison reverses the matched pattern and mixes other design choices; the distillation comparison also differs in codebook count and other choices, so both require caution.Source: §4.3 · p. 8
• Streaming has little effect at the first codebook but a larger effect at full depth; PAST reaches the 90% threshold earlier for three traits and later for emotion versus PAST-streamable.PAST has higher κ for four of seven traits at k = 1 and six at full depth; codec-group mean differences are small for most traits, except non-verbal sounds and age groups.Source: §4.3 · p. 8

4.4 Potential Future Applications

• Trait-depth profiles may guide codebook choices for voice conversion, speaker anonymization, and downstream analyses that need different portions of the stack.The authors also suggest the profiles may help reveal manipulation and detection signals.Source: §4.4 · p. 8

5 Conclusion

• Across 17 codecs, sex emerges from early codebooks, voice quality and age group from later ones, and other traits fall between.The analysis uses machine listeners and reweighted agreement on reconstructions from partial codebook stacks; trait depth is a proxy for the depth at which a trait is encoded and recovered.Source: §5 · p. 8
• Classes within a trait are not encoded uniformly, and codec design choices are associated with trait depths.The authors suggest temporal scales of encoded cues may possibly reflect the across-trait differences.Source: §5 · p. 8
• The findings motivate codec evaluations and system designs that account for which speaker and speech properties different codebook depths preserve and recover.Source: §5 · p. 8

• The protocol measures whether machine-listener predictions on partial reconstructions match predictions on original audio, not whether people perceive the traits as preserved.Decoding artifacts can also shift predictions, and a shallow relative trait depth can occur despite low absolute agreement.Source: Limitations · p. 9
• Different traits use datasets with substantially different sizes and speaker demographics, which may affect cross-trait comparisons and class-level estimate stability.For example, emotion uses 600 clips from 10 speakers, while voice quality uses 13,076 clips from 63 speakers.Source: Limitations · p. 9
• Codec-design comparisons are not fully controlled because most codecs differ in several design factors, limiting causal interpretation.Source: Limitations · p. 9
• Because test sets are predominantly English, the findings may not generalize to other languages.Source: Limitations · p. 9

• The analysis measures trait encoding and preservation using published classifiers on established datasets and reports aggregate results.Source: Ethical Considerations · p. 9
• The source labels and datasets have demographic and linguistic limitations, and the authors redistribute no audio.Source: Ethical Considerations · p. 9
• Although identifying trait-bearing codebooks may support anonymization and detection, it could also enable trait manipulation, so the authors release no editing tools.Source: Ethical Considerations · p. 9

AI Usage Disclosure

• AI assistance supported implementation, figure and table production, and text editing; the authors designed the study, reviewed outputs, and take responsibility for the paper.Source: AI Usage Disclosure · p. 9

A Neural Codec Details

• Table 3 lists decoded codebook prefixes: codecs with up to 12 codebooks use every prefix, while 25- and 32-codebook codecs use a sparser grid above eight.Inputs are resampled to each codec’s input rate and reconstructions back to 16 kHz; Table 3 marks the first eight of DAC’s 12 codebooks.Source: A Neural Codec Details · p. 13, Table 3 · p. 13

• Representative and auxiliary listeners’ κ scores correlate strongly: Spearman’s ρ ranges from 0.76 to 0.98 at k = 1 and 0.87 to 0.98 across all codec–depth points.Source: B Machine Listeners · p. 13, Table 4 · p. 13
• The analysis uses only representative machine listeners after the auxiliary listeners’ agreement scores align closely with theirs.Source: B Machine Listeners · p. 13
• Table 5 records clip, speaker, and audio-hour counts, while Table 7 lists each machine listener’s checkpoint, code, and license.Source: B Machine Listeners · p. 13, Table 5 · p. 13

C Compute Resources

• No model training is performed; all computation uses released codec checkpoints and machine listeners for inference and scoring.Source: C Compute Resources · p. 13
• About 1,400 GPU hours on NVIDIA A40, L40S, P100, and A100 GPUs is the approximate upper bound for total compute time.Source: C Compute Resources · p. 13
• Several workers shared one GPU, so actual compute time may differ from the estimate.Source: C Compute Resources · p. 13

D Additional Results

• The interactive page lets readers vary the κ threshold, inspect codec trait depths and per-prefix reweighted κ, compare listeners, and examine reweighting effects.It also supports per-class κ inspection codec by codec.Source: D Additional Results · p. 13
• Tables 6 and 7 list the codecs' and machine listeners' checkpoints, code, and licenses.Source: Table 6 · p. 14, Table 7 · p. 14
• Figure 4 shows an example screenshot of a view from the interactive webpage.Source: D Additional Results · p. 13, Figure 4 · p. 15