--- id: chen26t_interspeech title: "A Preclinical Study of Electrolaryngeal Voice Conversion for a Novel Nasal Electrolarynx: Feature Choice and Data Augmentation" authors: - Qi-Yan Chen - Ming-Chi Yen - Fo-Rui Li - Hsin-Te Hwang - Ching-Hung Lai - Shu-Wei Tsai - Ping-Cheng Yeh - Jyh-Shing Roger Jang - Yu Tsao - Hsin-Min Wang year: 2026 doi: 10.21437/Interspeech.2026-1882 isca_url: https://www.isca-archive.org/interspeech_2026/chen26t_interspeech.html pdf_url: https://www.isca-archive.org/interspeech_2026/chen26t_interspeech.pdf session: Beyond Speech Technologies in Healthcare topics: - speech-enhancement - voice-conversion - low-resource category: tts labels: - low-resource - generative-model institutions: - Academia Sinica - National Taiwan University - National Central University - National Cheng Kung University Hospital funding: - Taiwan National Science and Technology Council code: url: https://ymchiqq.github.io/nelvc_demo/ license: "" open_to_collaboration: false wiki_frontmatter: id: chen26t_interspeech category: tts labels: - low-resource - generative-model institutions: - Academia Sinica - National Taiwan University - National Central University - National Cheng Kung University Hospital code: https://ymchiqq.github.io/nelvc_demo/ updated: 2026-09-29 confidence: full-paper digest: v2 source: https://doi.org/10.21437/Interspeech.2026-1882 pdf: https://www.isca-archive.org/interspeech_2026/chen26t_interspeech.pdf wiki_url: https://interspeech-2026-wiki.vercel.app/papers/chen26t_interspeech/ markdown_url: https://interspeech-2026-wiki.vercel.app/papers/chen26t_interspeech/markdown.md --- # A Preclinical Study of Electrolaryngeal Voice Conversion for a Novel Nasal Electrolarynx: Feature Choice and Data Augmentation *Qi-Yan Chen, Ming-Chi Yen, Fo-Rui Li, Hsin-Te Hwang, Ching-Hung Lai, Shu-Wei Tsai, Ping-Cheng Yeh, Jyh-Shing Roger Jang, Yu Tsao, Hsin-Min Wang* [PDF](https://www.isca-archive.org/interspeech_2026/chen26t_interspeech.pdf) · [ISCA page](https://www.isca-archive.org/interspeech_2026/chen26t_interspeech.html) · [DOI](https://doi.org/10.21437/Interspeech.2026-1882) **Category:** `tts` · **Labels:** `low-resource`, `generative-model` **TL;DR** — This study presents the first systematic electrolaryngeal voice conversion (ELVC) system for a newly invented nasal electrolarynx (NEL), introducing an LLE-VC data augmentation method that improves Character Error Rate (CER) from 72.0% to 63.0%. ## Key contributions - First systematic investigation of voice conversion for a nasal electrolarynx (NEL), contrasting its acoustics with traditional cervical electrolarynx (CEL) devices. - Demonstrates that optimal feature representation is device-dependent: WavLM features excel for CEL, while 80-channel Mel-spectrograms better preserve NEL's unique mid-high-frequency resonances. - Proposes a lightweight LLE-VC-based data augmentation strategy using a 10,000-sentence corpus (TWnews) to generate paired sNEL-sNL training data without training a dedicated TTS model. ## Problem Traditional cervical electrolarynges (CEL) suffer from loud mechanical excitation noise, rigid pitch, and awkward neck placement. A recently invented nasal electrolarynx (NEL) places the vibrator at the entrance of the nasal cavity for internal acoustic coupling, reducing mechanical noise and enabling hands-free operation. However, NEL speech exhibits unusual acoustic patterns—such as severe low-frequency attenuation and prominent mid-high-frequency nasal cavity resonances—causing standard CEL-oriented voice conversion and general zero-shot VC baselines to fail. ## Method The system builds upon sequence-to-sequence transformer VC frameworks (VTN-VC and its advanced variant ETN-VC). The training uses a multi-stage recipe: (1) pretraining an acoustic decoder on 44 hours of COSPRO dataset text-to-speech data, (2) freezing the decoder while aligning encoder representations to speech inputs, and (3) fine-tuning on parallel electrolaryngeal-to-natural (EL-NL) pairs. ETN-VC introduces an intermediate pretraining stage leveraging synthetic pairs generated via Locally Linear Embedding VC (LLE-VC). Using F5-TTS on the 10k-sentence TWnews corpus, natural text is synthesized to natural speech (sNL) and then converted to synthetic NEL (sNEL) via LLE-VC using 6th-layer WavLM-Large neighbor retrieval. For input features, the model contrasts 1024-dimensional WavLM layer-6 embeddings against 80-dimensional Mel-spectrograms. Mel-spectrograms are directly constructed via LLE-VC neighbor matching to retain sharp spectral peaks, bypassing vocoder-induced degradation present in feature reconstruction. ## Experimental setup Evaluated on Mandarin Chinese TMHINT sentences (320 utterances by a healthy speaker; 240 train, 40 dev, 40 test). NEL speech was recorded in a hospital ward to mirror real clinical acoustics, while NL and CEL were recorded in a studio. Evaluated via Mel-Cepstral Distortion (MCD), F0 RMSE, F0 CORR, duration error (DDUR), Character Error Rate (CER via Whisper-Large), Syllable Error Rate (SER), SpeechBERTScore, UTMOS, MOSA-Net+, and 30-participant A/B listening tests. ## Results ETN-VC with Mel-spectrograms reduces CER from 72.0% down to 63.0% and SER from 58.8% down to 53.8% on NEL-to-NL conversion when scaling synthetic pretraining data from 1k to 10k utterances. While WavLM-based models achieve higher non-intrusive quality scores (UTMOS 2.998 vs 2.666 for Mel), Mel-spectrogram models decisively win human A/B intelligibility tests and yield lower ASR error rates because they successfully preserve critical mid-high-frequency nasal resonances that WavLM flattens out. | System | Feature | CER (%) ↓ | SER (%) ↓ | UTMOS ↑ | |---|---|---|---|---| | Unprocessed NEL | ✗ | 90.8 ± 2.4 | 89.5 ± 6.3 | 1.30 | | VTN-VC | Mel-spectrogram | 72.0 ± 6.6 | 58.8 ± 6.4 | 2.47 | | VTN-VC | WavLM-layer6 | 71.5 ± 7.7 | 62.0 ± 5.6 | 2.94 | | ETN-VC (10k sNEL) | Mel-spectrogram | 63.0 ± 7.4 | 53.8 ± 4.8 | 2.67 | | ETN-VC (10k sNEL) | WavLM-layer6 | 65.8 ± 7.4 | 54.3 ± 5.6 | 3.00 | ## Limitations The study is strictly preclinical, relying on a single healthy speaker simulating NEL speech rather than actual laryngectomy patients. Evaluation is restricted to Mandarin Chinese, and tests only a single architectural backbone and select SSL layers. ## Why read this Speech engineers and medical audio researchers working on voice restoration should read this to see how self-supervised feature representations break down on alternative excitation mechanisms like nasal electrolarynges, and why classical Mel-spectrograms combined with LLE augmentation outperform SSL for high-distortion speech reconstruction. ## Code - https://ymchiqq.github.io/nelvc_demo/ ## Applications Assistive speech restoration, real-time voice conversion for laryngectomy patients using nasal electrolarynx hardware, and biomedical voice prosthetics. ## Institutions / 機構 Academia Sinica, National Taiwan University, National Central University, National Cheng Kung University Hospital **Funding / 經費:** Taiwan National Science and Technology Council ## Related - [One-to-Many Electrolaryngeal Voice Conversion with Synthetic Data](wu26l_interspeech.md) — same problem · relatedness 2.6/3 - [Personalized Electrolaryngeal Voice Conversion with a Single Pre-operative Utterance](chang26e_interspeech.md) — same problem · relatedness 2.2/3 - [CFLOW-VC: An unsupervised cycle training strategy based on normalizing flows for Voice Conversion](song26_interspeech.md) — same problem · relatedness 2.0/3 - [SSL-GMMVC: Interpretable Voice Conversion via Locally Linear GMM Transforms in Self-Supervised Representation Space](tanabu26_interspeech.md) — same problem · relatedness 1.9/3 - [Synthetic Audio Generation Framework for Air Traffic Control Speech Recognition](bagat26_interspeech.md) — shared technique · relatedness 1.9/3 All 950k paper pairs scored by TypeSafe Jev (`scripts/related/`); relatedness 0 = unrelated … 3 = directly comparable.