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CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing
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Proposes CA-SSLR, which freezes an SSL speech encoder (XLSR, mHuBERT) and inserts trainable time-channel attention conditioners that modulate layers using intermediate language and speaker embeddings, improving multilingual ASR, LID and speaker verification.
Problem
Standard fine-tuning of self-supervised speech encoders for downstream tasks hurts generalization to unseen tasks and causes forgetting, and SSL features are not aware of language or speaker context in multilingual settings.
Approach
Hierarchically condition frozen SSL encoder layers on intermediate LID and SV embeddings via linear modulation (scale and bias) with time-channel attention conditioners, initialized to identity so the pretrained behavior is preserved at the start of training.
Key results
- CA-XLSR(L,S) with CC reduces VoxCeleb1 EER from 1.29 to 0.94 at 1h, about 27% relative, versus the frozen XLSR baseline.
- CA-XLSR(L) with TCAC lowers 1h ASR CER from 22.7 to 15.1 on normal languages (about 33% relative) and raises LID accuracy from 90.9 to 93.4.
- Full fine-tuning of XLSR degrades SV EER from 1.29 to 7.23 at 10 min, while CA-SSLR preserves or improves SV performance.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add hierarchical language conditioning with a separate LID embedding pass | XLSR · ML-SUPERB 10-min, Normal | ASR CER: 29.0 -> 21.7 | Helped |
| Add hierarchical language conditioning with a separate LID embedding pass | XLSR · ML-SUPERB 10-min, Few-shots | ASR CER: 39.0 -> 33.4 | Helped |
| Add language conditioning with TCAC | XLSR · ML-SUPERB 10-min, Normal | ASR CER: 29.0 -> 18.6 | Helped |
| Add a speaker conditioner to language conditioning | CA-XLSR L · ML-SUPERB, 10-min and 1-hour | ASR CER: 3.1%; 6.2% | Helped |
| Add a speaker conditioner to language conditioning | CA-XLSR L · VoxCeleb1, 10-min and 1-hour training sets | SV EER: 19.4-27.1% | Helped |
| Add TCAC time-wise attention instead of channel-wise conditioning (ablation) | CA-XLSR L dual · ML-SUPERB 10-min, Normal and Few-shots | ASR CER: 18.6 -> 17.8; 32.2 -> 31.8 | Helped |
| Use channel-wise conditioning instead of TCAC (ablation) | CA-XLSR L,S · VoxCeleb1, 10-min and 1-hour training sets | SV EER: 1.11 -> 1.04; 1.01 -> 0.94 | Helped |
| Add TCAC time-wise attention instead of channel-wise conditioning (ablation) | CA-XLSR L,S · ML-SUPERB 10-min and 1-hour | ASR CER: 18.8 -> 18.3; 15.0 -> 14.4 | Helped |
| Use CC instead of TCAC (ablation) | CA-XLSR L,S · VoxCeleb1, 10-min and 1-hour training sets | SV EER: 1.11 -> 1.04; 1.01 -> 0.94 | Mixed |
| Fine-tune the pretrained encoder instead of keeping it frozen | XLSR · ML-SUPERB 10-min + VoxCeleb | LID accuracy: 89.0 -> 81.5 | Hurt |
| Fine-tune the pretrained encoder instead of keeping it frozen | XLSR · ML-SUPERB 10-min + VoxCeleb | SV EER: 1.29 -> 7.23 | Hurt |
| Condition on hard-predicted LID labels instead of language embeddings (ablation) | CA-XLSR L dual · ML-SUPERB 10-min, Few-shots | ASR CER: 26% relative improvement over baseline | Mixed |
Surprising findings
- Full fine-tuning on multiple tasks degrades performance below the frozen baseline due to conflicting adaptation.
- Houlsby adapters and full fine-tuning degrade SV performance when adapted only for ASR, whereas the conditioned model improves it.
Limitations
- Conditioned models are 13-34% slower at inference and training uses more memory than Houlsby adapters.
- Full fine-tuning baseline may be suboptimally tuned, and gains are reported on a moderate-size benchmark (ML-SUPERB 10-min/1-h).
- Code is promised but no release link is given.
Experimental setup
Datasets: ML-SUPERB VoxCeleb1 VoxCeleb2 MUSAN RIR reverberation
Benchmarks: ML-SUPERB VoxCeleb1
Metrics: LID accuracy CER EER minDCF real-time factor
Baselines: XLSR fine-tuning Houlsby adapters LoRA mHuBERT MMS-1b
Builds on: wav2vec 2.0 ML-SUPERB Houlsby adapters FiLM ECAPA-TDNN
Closest papers by meaning
- Unified Speech Recognition: A Single Model for Auditory, Visual, and Audiovisual Inputs (NeurIPS 2024 · similarity 0.85)
- SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic Soundscapes (ICLR 2025 · similarity 0.85)
- Multi-resolution HuBERT: Multi-resolution Speech Self-Supervised Learning with Masked Unit Prediction (ICLR 2024 · similarity 0.85)
- SelfVC: Voice Conversion With Iterative Refinement using Self Transformations (ICML 2024 · similarity 0.85)
- Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models (NeurIPS 2024 · similarity 0.85)
- The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning (ICML 2025 · similarity 0.84)
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