Towards Interpretable Framework for Neural Audio Codecs via Sparse Autoencoders: Exploration toward Age, Gender, and Accent Steering

• NAC research has emphasized downstream task benchmarks, while limited interpretability constrains explaining and verifying codec-based systems in sensitive applications.Source: §1 · p. 1
• Prior SAE analysis examined accent at the task level, but did not test waveform-level effects or whether findings generalize to other speaker traits.Source: §1 · p. 1
• The authors use SAE activation steering as a proxy: intended shifts in reconstructed-speech attribute predictions suggest trait information is decomposed into selected dimensions.Source: §1 · p. 1
• The study extends analysis beyond accent to age and gender and frames its goal as interpretability, not attribute conversion.Source: §1 · p. 1
• Across five NACs, steering shifts trait predictions; gender control is most consistent, stronger steering is not reliably stronger, and content and quality generally degrade.Source: §1 · p. 1

2.1. Neural Audio Codec Representations

• NACs encode a waveform into quantized frame embeddings and decode those embeddings into a reconstructed waveform.The encoder produces e = E(x), quantization yields codes and embeddings, and the decoder maps embeddings back to speech.Source: §2.1 · pp. 1–2

2.3. SAE Steering

• Trait-associated SAE dimensions are selected and modified to steer reconstructed speech from a source trait toward a target trait.The method uses reverse steering and varies α to assess steering strength.Source: Figure 1 · p. 2, §2.3 · p. 2

2.2. Sparse Autoencoders (SAEs)

• A Top-k SAE represents each dense codec frame with sparse activations by retaining its k largest activations and zeroing the rest.A linear decoder reconstructs each frame, and the reconstructed frame sequence is decoded into speech.Source: §2.2 · p. 2

2.3. SAE Steering

• Trait associations are estimated from activation frequency across frames, counting repeated frame-level activations rather than utterance-level occurrence.Dimensions are ranked by their comparison score, and the top M candidates associated with each trait are retained.Source: §2.3 · p. 2
• High activation frequency alone does not establish trait specificity because dimensions may reflect linguistic or acoustic information shared across traits.Real recordings do not provide the matched text and speaker controls used in prior work, and speaker traits lack a natural neutral baseline.Source: §2.3 · p. 2
• Steering changes only dimensions in the original active set, subtracting α from source-associated dimensions and adding α to target-associated dimensions.Rarely activated or dead latents are also suppressed in the selection procedure.Source: §2.3 · p. 2

3.1. Dataset

• Vox-Profile combines 11 open-source English-speech datasets with self-reported speaker traits and speaker-disjoint train, validation, and test splits; steering dimensions are selected using labeled test waveforms.The evaluation covers young adults (18–30) versus seniors (over 60), female versus male, US versus UK accents, and US versus Southern Asian accents.Source: §3.1 · pp. 2–3

3.2. Neural audio codecs

• The five evaluated codecs are EnCodec, DAC, SpeechTokenizer, Mimi, and Qwen3-TTS-Tokenizer-12Hz.Qwen3-TTS-Tokenizer-12Hz is added to the four codecs used in prior work.Source: §3.2 · p. 3

3.3. Training & Hyperparameters

• SAEs use a latent dimension of 10 times the codec dimension and sparsity k = ⌈0.1 · dcodec⌉, scaling both settings with each codec’s representation size.Models are trained for 300 epochs; steering uses λ=100 and M=30.Source: §3.3 · p. 3

3.4. Speaker Trait Classifiers and Baselines

• Age and gender predictions come from Wav2Vec 2.0, while accent predictions use Vox-Profile’s broad-accent classifier, mapping Southern Asian accents to Other.Reported outcomes are predicted age, female probability, and pairwise US probability; baseline predictions establish reconstruction-induced differences before steering.Source: §3.4 · p. 3, Table 1 · p. 3
• A Mimi baseline steers M randomly selected SAE dimensions to test whether the algorithm-selected dimensions produce larger speaker-trait shifts.The comparison is motivated by the expectation that random dimensions should shift traits less if trait information is concentrated in specific dimensions.Source: §3.4 · p. 3

4.1. Steering Results

• Across five NACs, steering produces target-directed trait shifts, but age and accent responses vary substantially by codec and steering direction.Senior-to-young steering lowers predicted age across codecs, whereas young-to-senior responses are inconsistent; US-to-SA steering is less successful than SA-to-US steering.Source: §4.1 · pp. 3–4
• Gender steering is target-directed across NACs: female probability falls toward zero for female-to-male steering, while male-to-female responses are weaker for DAC and EnCodec.Mimi, SpeechTokenizer, and Qwen tokenizer reach female probabilities close to 1 at α=20 for male-to-female steering.Source: §4.1 · p. 4

4.2. Dimension Selection and Steering Strength

• Randomly selected Mimi dimensions produce smaller target-directed shifts than algorithm-selected dimensions, especially for gender, where predictions remain near the SAE baseline.Random steering also yields smaller shifts for age and accent.Source: §4.2 · p. 4
• The best steering strength varies across codecs and traits, and increasing α does not consistently strengthen the intended shift.Some accent responses are non-monotonic as α increases.Source: §4.2 · p. 4, §4.1 · p. 4

4.3. Steering Quality Analysis

• Steering generally increases WER and lowers SHEET quality scores relative to unsteered SAE reconstruction.The relationship between trait-shift magnitude and degradation varies across codecs; Mimi and SpeechTokenizer preserve content and perceptual quality comparatively well in several settings, sometimes with modest trait shifts.Source: §4.3 · p. 4, Figure 3 · p. 4
• The observed quality degradation suggests selected SAE features do not fully isolate speaker traits from other speech information.In one comparison, Mimi and Qwen tokenizer produce comparable female-probability shifts, while Mimi has lower WER and higher SHEET scores.Source: §4.3 · p. 4

• Across five NACs, steering selected dimensions produces target-directed shifts in speaker-trait predictions, and random-dimension comparisons in Mimi support the selected dimensions’ relevance.Source: §5 · p. 4
• The analysis extends prior accent work to waveform-level investigation of age, gender, and accent using SAE steering as an interpretability proxy.Source: §5 · p. 4
• Responses vary across NACs, traits, and steering directions; stronger steering does not consistently amplify intended shifts, and steering degrades speech content and perceptual quality.These effects suggest the SAE does not fully isolate speaker traits from other speech information.Source: §5 · p. 4

• The study uses existing Vox-Profile datasets, and ethical approval was not required under applicable institutional policies.Source: §6 · p. 5