Auxiliary-Sensor Speech Enhancement (AS-SE)

• Auxiliary-Sensor Speech Enhancement (AS-SE) is a set of techniques that integrate audio with auxiliary modalities such as visual cues, biosignals, and environmental inputs to enhance speech clarity.
• It employs advanced deep multimodal architectures with strategies like late fusion, multi-task learning, and personalized conditioning to optimize noise suppression and intelligibility.
• AS-SE applications span hearing aids, telecommunication ASR systems, and human–robot interactions, evaluated using metrics such as PESQ, STOI, and SI-SDR.

Auxiliary-Sensor Speech Enhancement (AS-SE) refers to a class of methods and systems that incorporate additional sensory data beyond the conventional single or multi-microphone speech input to improve noise robustness, intelligibility, and downstream utility of enhanced speech signals. AS-SE leverages diverse auxiliary modalities—including visual inputs (lip, face, scene context), biosignals (electromyography), in-ear microphones, speaker embeddings, and environmental cues—to address challenges inherent in low-SNR, highly variable, or interference-prone acoustic environments. Techniques span deep multimodal architectures, multi-task optimization, conditional noise suppression, meta-evaluation-driven optimization, and robustness to varying sensor array designs and data availability constraints.

1. Core Methodologies of AS-SE

AS-SE encompasses a broad array of architectures, each designed around the specific modality and fusion strategy:

• Multimodal Feature Fusion: Models such as CochleaNet ingest both noisy audio and visual cues (e.g., lip images), extracting features through specialized convolutional and recurrent layers. Audio features are typically derived from the STFT spectrogram, while visual cues are processed from cropped region sequences. Synchronization between modalities is achieved by upsampling lower-frame-rate features before concatenation and fusion using layers such as LSTM or fully connected blocks (Gogate et al., 2019).
• Auxiliary Bio-Signals: EMGSE and related frameworks incorporate electrodes on the face, particularly the cheek, to record muscular activity corresponding to speech articulation. Preprocessing comprises bandpass filtering, time-domain feature extraction (mean, power, ZCR), and large-scale feature stacking to capture temporal context. Late fusion of high-dimensional EMG latent vectors with audio encodings is realized using fully connected layers, driving BLSTM-based enhancement networks (Wang et al., 2022, Feng et al., 11 Jan 2025).
• Scene Context and Visual Cues: SAV-SE uniquely expands auxiliary-sensor input to environmental visual context. Pretrained encoders (e.g., CAV-MAE) extract semantic scene embeddings, which combine with spectral and audio features. The enhancement backbone leverages joint Conformer and selective state-space modules (Mamba), applying bidirectional and convolutional processing blocks to model temporal-spatial dependencies and estimate phase-sensitive masks (Qian et al., 12 Nov 2024).
• In-Ear and Peripheral Microphone Arrays: PAS-SE explores the synergy of in-ear microphones (body-conduction, high SNR but band-limited and distorted) with outer microphones. The FT-JNF architecture fuses multi-microphone features along frequency and time axes using LSTM blocks, outputting magnitude masks for target separation. EnroLLMent-based personalization can be composited with AS-SE to further resolve target/interferer ambiguities (Ohlenbusch et al., 25 Sep 2025).
• Auxiliary Scalar Inputs: The SNRi Target Training method conditions the speech enhancement network on a dynamically predicted scalar indicating the desired SNR improvement. This auxiliary input is estimated through additional neural networks and concatenated with encoder representations. Training is joint with downstream ASR tasks to achieve global optimization for both enhancement and recognition (Koizumi et al., 2021).

2. Optimization Strategies and Fusion Mechanisms

The fusion of auxiliary data in AS-SE employs several complementary strategies:

• Late Fusion versus Early Fusion: Most contemporary systems favor late fusion for heterogenous sensor inputs to allow domain-specific feature encoders. For instance, EMGSE fuses normalized EMG features post-encoding to facilitate robust joint modeling without degrading the individual signal’s representational integrity (Wang et al., 2022).
• Multi-task Learning: Some AS-SE approaches, e.g., those incorporating speaker-aware adaptation, optimize for both the enhancement target (such as an ideal binary mask or time-frequency mask estimation) and auxiliary tasks (speaker recognition, embedding extraction). Loss functions aggregate SDR, cross-entropy, and mask-based criteria (Koizumi et al., 2020).
• Personalization and Conditioning: PAS-SE and related frameworks allow networks to be conditioned using learned speaker representations from enroLLMent utterances, coupled multiplicatively or additively to core network features. This explicit conditioning focuses the enhancement process on the target user, resolving ambiguous mixtures (Ohlenbusch et al., 25 Sep 2025).
• Meta-Learning and Proxy Objectives: Training AS-SE models with supervisory signals from pretrained evaluation models (e.g., SQA) enables multi-metric, evaluation-aligned optimization—even when clean references are unavailable for real-world data. Losses are blended across score-based, feature-space, and regularization terms to prevent adversarial model behavior (Wang et al., 13 Jun 2025).

3. Evaluation Protocols and Performance Metrics

AS-SE systems are assessed across multiple objective and perceptual metrics:

Metric	Domain	Purpose
PESQ	Auditory Quality	Signal fidelity
STOI	Intelligibility	Speech clarity
SI-SDR	Distortion Reduction	Separation
MOS Variants	Human Perception	Quality ratings
CER/WER	ASR Incidence	Text accuracy

For illustration, CochleaNet achieves PESQ scores up to 2.85 at 9 dB SNR and STOI values of 0.521 at –12 dB SNR (Gogate et al., 2019). EMGSE improves PESQ by ~0.225 and STOI by ~0.097 over audio-only SE in challenging low-SNR settings (Wang et al., 2022), while multi-modal EMG-based SE gains up to 0.527 in PESQ under mismatched noise (Feng et al., 11 Jan 2025). PAS-SE demonstrates superior SI-SDR, PESQ, and ESTOI for both in-domain and cross-dataset tests, particularly with in-ear enroLLMents robust against noise (Ohlenbusch et al., 25 Sep 2025).

4. Generalization, Adaptation, and Robustness

AS-SE approaches are designed to generalize across languages, speakers, array designs, and noise environments:

• Language and Speaker Independence: Models such as CochleaNet trained on modest-size, English-language corpora (Grid, CHiME3) generalize to large-vocabulary tasks and Mandarin datasets despite vocabulary and acoustic mismatches (Gogate et al., 2019).
• Cross-Dataset Adaptation: PAS-SE protocols employ explicit training data augmentations—injecting noise and simulated interferers into both in-ear and outer microphones—to foster generalization across sensor arrays and recording conditions. Approximating in-ear interferers as scaled outer signals allows robust learning without dataset-specific overfitting (Ohlenbusch et al., 25 Sep 2025).
• Performance with Missing or Noisy Auxiliary Inputs: Studies show that AV fusion models degrade gracefully as auxiliary (e.g., visual) cues are occluded, maintaining performance with up to 20% occlusion and defaulting to audio-only baseline when visuals are absent (Gogate et al., 2019). PAS-SE remains robust with noisy in-ear enroLLMents due to acoustic shielding (Ohlenbusch et al., 25 Sep 2025).

5. Applications and Operational Implications

AS-SE methods show promise for diverse deployments:

• Hearing Assistance and Hearables: The causal, low-latency AV and bio-signal SE systems enable improved intelligibility in real-world noisy contexts (cafeterias, public spaces) for hearing aid and cochlear implant users (Gogate et al., 2019, Ohlenbusch et al., 25 Sep 2025).
• Telecommunications and ASR Frontends: SNR-adaptive SE systems, and speech enhancement architectures trained on recognition-related metrics (CER-centric or WER-centric), deliver improved ASR accuracy in variable noise and reverberation, relevant for voice-controlled devices and call-center applications (Sawata et al., 2021, Koizumi et al., 2021).
• Assistive Devices and Silent Speech Interfaces: EMG fusion-based SE supports robust speech transmission even for silent or low-audibility articulation, with practicable systems based on as few as 8 EMG channels (Feng et al., 11 Jan 2025).
• Human–Robot Interaction and Scene-Aware Systems: SAV-SE demonstrates the benefit of environmental visual context for noise suppression when facial cues are unavailable—expanding AS-SE’s reach to smart devices and robotics in noisy, visually complex scenarios (Qian et al., 12 Nov 2024).

6. Open Issues and Future Directions

Active research challenges in AS-SE entail:

• Fusion of Heterogeneous Modalities: Current cross-modality fusion modules, while effective, leave room for improvement in feature integration and alignment—particularly when modalities differ drastically in bandwidth, reliability, or informativeness (Feng et al., 11 Jan 2025).
• Training on Real-World Data: SQA-driven meta-supervision frameworks circumvent the lack of clean references but require careful regularization to avoid adversarial exploitation of evaluation models. Expanding SQA models to incorporate further sensors holds promise for future multi-metric, multi-modal systems (Wang et al., 13 Jun 2025).
• Enhancement in Highly Adverse Conditions: Extending robustness to more extreme SNRs, highly non-stationary/interfering noise, and dynamically missing auxiliary data remains a significant focus. Methods such as PAS-SE with personalized and auxiliary input fusion exemplify trends toward high-reliability solutions in ever-changing operational scenarios (Ohlenbusch et al., 25 Sep 2025).
• Sensor Array Scalability and Practicality: Reductions in required auxiliary sensor channels (e.g., transitioning from 35-channel EMG to 8-channel configurations) enhance prospective real-world adoption, but further work is needed in adaptive sensor placement, feature selection, and wearable hardware interfaces (Feng et al., 11 Jan 2025, Wang et al., 2022).

7. Summary Table: Representative AS-SE Modalities and Techniques

Approach	Auxiliary Modality	Fusion Strategy	Advantages
CochleaNet (Gogate et al., 2019)	Lip images	Dilated Conv + LSTM fusion	Language/noise independence, AV generalization
SAV-SE (Qian et al., 12 Nov 2024)	Scene video context	Multi-encoder + selective SSM	Ambient noise type suppression
EMGSE (Wang et al., 2022)	Cheek EMG	Late fusion, BLSTM	Improved low-SNR performance
Multi-modal with SEMamba (Feng et al., 11 Jan 2025)	8-channel EMG	DenseEncoder, TF-Mamba fusion	Robust with fewer sensors
PAS-SE (Ohlenbusch et al., 25 Sep 2025)	In-ear mic + speaker enroll.	FT-JNF + conditioning	Robust own-voice pickup, interferer suppression
SNRi Target Training (Koizumi et al., 2021)	Scalar (target SNRi)	Conditioned encoder	Adaptive enhancement/ASR

The continued evolution of auxiliary-sensor speech enhancement is marked by increased generalizability, multimodal data fusion, training methodology innovation, and adaptability to real-world operational constraints. As models draw from environmental context, biosignals, embedded arrays, and meta-evaluation, the domain stands poised to address long-standing challenges in noise-robust, highly intelligible speech communication and recognition.