Low-Resource TTS Synthesis

TTS: Text-to-Speech

Low-resource text-to-speech (TTS) aims to produce intelligible, natural speech for languages and dialects with few recordings, often including tonal languages and non-Latin scripts. Common approaches combine curated speech corpora with cross-lingual transfer from multilingual or foundation models, parameter-efficient fine-tuning, and synthetic data or pseudo-labeling; text normalization and language-specific phoneme, tone, and prosody modeling are also important. Evaluation typically combines listening tests such as mean opinion scores with intelligibility, speaker-similarity, and acoustic measures, increasingly testing long or complex text and out-of-domain use. More data and larger pretrained models can improve quality, but synthetic-data errors, weak language coverage, and trade-offs among intelligibility, expressivity, and speaker identity remain open challenges.

Latest papers

32 papers4 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

SEAM: Spectral Embedding Alignment and Merging for Low-Resource TTS
Oct 19, 2026Walaa Othman, Baher Mohammad, Stamatios Lefkimmiatis +1Text-to-Speech Low-Resource TTS Synthesis
ITMO University · SPC RAS · MWS AI
ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies
Oct 14, 2026Sangmin Lee, Yejin Jeon, David Ifeoluwa AdelaniLow-Resource Language Processing Multilingual ASR
Yonsei University · McGill University · Mila - Quebec AI Institute +1
BanglaBox: A Phonetically-Balanced Corpus and Data-Efficient Foundation-Model Adaptation for Bangla Text-to-Speech with Zero-Shot Voice Cloning
Oct 6, 2026Emtiaz Uddin Ahmed, Araf Mahmud, Sajib Hossain +3Text-to-Speech Low-Resource TTS Synthesis
A Comprehensive Objective Evaluation of Modern Text-to-Speech for Turkish Using Speech Quality Assessment Models
Oct 5, 2026Yunus Emre Ozkose, Alperen Kahraman, Ali HaznedarogluTTS Synthesis TTS Evaluation
Sestek, Ankara, Türkiye
BanglaKontho: Closing the Long-Form Gap in Bangla Text-to-Speech
Sep 24, 2026Mizbaul Haque MarufTTS Synthesis Low-Resource TTS Synthesis
Vivasoft Limited, Dhaka, Bangladesh
From Reliable Text to Real Voices: Trust-Aware Progressive Adaptation for Low-Resource TTS
Sep 22, 2026Jiayi Lu, Yizhong Geng, Jinghan Yang +4Synthetic-to-Real Domain Adaptation Low-Resource TTS Synthesis
Beijing Logic Intelligence Technology · University of Washington · Beijing University of Posts and Telecommunications +2
Complex-Text Robustness Evaluation and Failure Diagnosis for Low-Resource Multilingual Text-to-Speech
Sep 10, 2026Tianlun Zuo, Ziyu Zhang, Tingzhi Mao +2TTS Synthesis TTS Evaluation
iFLYTEK Company Ltd., Xi’an, China
Assessing the Reusability of Public Speech Resources for Low-Resource Languages: A Central Kurdish Case Study
Sep 10, 2026Hiwa AsadpourSpeech Processing Low-Resource TTS Synthesis
University of Saarland and Goethe University Frankfurt
Deterministic Prompting for Speaker-Stable Low-Resource Greek TTS
Sep 9, 2026Georgios Syllas, Efthymios Georgiou, Kosmas Kritsis +1TTS Synthesis Speech Processing
Institute for Language and Speech Processing, Athena R.C., Greece · Department of Digital Medicine, University of Bern, Switzerland · School of Electrical and Computer Engineering, National Technical University of Athens, Greece
Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech
Sep 3, 2026Kunat Pipatanakul, Potsawee Manakul, Warit Sirichotedumrong +3TTS Synthesis TTS Evaluation
Wayu Research · Paxa Labs · Typhoon
Iterative Self-Learning for Expressive Text-to-Speech Synthesis
Aug 16, 2026Nicholas Sanders, Gustav Eje Henter, Simon King +1Expressive Speech Synthesis TTS Synthesis
University of Edinburgh
Domain-Specific Evaluation of Text-to-Speech Systems: A Multi-Metric Benchmarking Study
Aug 3, 2026Ali Jafar, Amal Sarmad, Shifa Yousaf +1Speech Generation Evaluation TTS Evaluation
FAST School of Computing, National University of Computer and Emerging Sciences (FAST-NUCES), Lahore, Punjab, Pakistan
Staged Depth-Pruning Distillation of a Flow-Matching Text-to-Speech Teacher: A Compact Hindi Speech Synthesizer
Jul 19, 2026Sivateja TrikutamTTS Synthesis Low-Resource TTS Synthesis
A Situational Speech Synthesizer for Yoruba: System Design, Phonological Rule Architecture, and Orthographic Extensions for Contour
Jul 17, 2026Kola Tubosun, Adedayo Oluokun, Hafiz Adewuyi +1TTS Synthesis Low-Resource TTS Synthesis
Towards Digital Preservation of Efik: TTS for a Low-Resource African Language
Jul 5, 2026Offiong Bassey Edet, Emmanuel Oyo-Ita, Archibong Okon Archibong +2TTS Synthesis Speech Generation Evaluation
University of Cross River State, Nigeria · ML Collective · University of Calabar, Nigeria
LuxEmo: Expressive Text-to-Speech Corpus for Luxembourgish
Jun 30, 2026Nina Hosseini-Kivanani, Sandipana DowerahSpeech Processing Low-Resource TTS Synthesis
Radio T´el´evisioun L¨etzebuerg (RTL) & University of Luxembourg · Esch-sur-Alzette, Luxembourg · Tallinn University of Technology +1
Closing the Quality Gap in Low-Resource Text-to-Speech: LoRA Fine-Tuning of VoxCPM2 for Khmer and Korean
Jun 25, 2026Phannet Pov, Hyun Woo Park, Voneat Pen +3Low-Resource TTS Synthesis Low-Resource Language Processing
Department of Big Data, Chungbuk National University, Cheongju, South Korea · Institute of Digital Research and Innovation, Cambodia Academy of Digital Technology, Phnom Penh, Cambodia · Department of Management Information Systems, Chungbuk National University, Cheongju, South Korea +1
Exploring Pre-training Benefits on Phoneme Addition through Fine-tuning in Speech Synthesis
Jun 18, 2026Masato Murata, Koichi Miyazaki, Tomoki Koriyama +1TTS Synthesis Low-Resource TTS Synthesis
CyberAgent, Japan · Nagoya University, Japan
OpenBibleTTS: Large-Scale Speech Resources and TTS Models for Low-Resource Languages
Jun 8, 2026David Guzmán, Luel Hagos Beyene, Jesujoba Oluwadara Alabi +3TTS Synthesis TTS Evaluation
McGill University · Mila - Quebec AI Institute · AIMS Research and Innovation Centre +3
NüshuVoice: Reviving the Voice of Endangered Nüshu with Pitch-Aware Text-to-Speech
Jun 8, 2026Hongkun Yang, Xinhui Yi, Xiyan Zhao +13TTS Synthesis Low-Resource TTS Synthesis
1Ocean University of China · 2The Hong Kong Polytechnic University · 3Cornell University +5
PashtoTTS-Bench: automated screening for low-resource non-Latin-script text-to-speech
May 26, 2026Hanif RahmanSpeech Generation Evaluation TTS Evaluation
aIndependent Researcher
Tibetan-TTS:Low-Resource Tibetan Speech Synthesis with Large Model Adaptation
May 4, 2026Jiaxu He, Chao Wang, Jie Lian +4TTS Synthesis Low-Resource TTS Synthesis
Xingchen AGI Lab, China Telecom Artificial Intelligence Technology Co. Ltd · Qinghai Normal University, Xining, China · University of Electronic Science and Technology of China,Chengdu,China +1
SPARCLE: SPeaker-aware Aligned Representations via Contrastive Language Embeddings
May 1, 2026Priyam Mazumdar, Yurii Halychanskyi, Steven Guo +2Representation Learning TTS Synthesis
University of Illinois Urbana-Champaign, USA · National Center for Supercomputing Applications, USA
Giving Voice to the Constitution: Low-Resource Text-to-Speech for Quechua and Spanish Using a Bilingual Legal Corpus
Apr 14, 2026John E. Ortega, Rodolfo Zevallos, Fabricio CarraroMultilingual TTS Synthesis TTS Synthesis
Northeastern University, USA · Universitat Pompeu Fabre, Spain · Barcelona Supercomputing Center, Spain
Bridging the Stability-Expressivity Gap: Synthetic Data Scaling and Preference Alignment for Low-Resource Spoken Language Models
Apr 10, 2026Yizhong Geng, Yanliang Li, Jinghan Yang +4TTS Synthesis Speech Language Models
Beijing University of Posts and Telecommunications, Beijing, China · Beijing Logic Intelligence Technology, Beijing, China · University of California, USA +2
Tagarela - A Portuguese speech dataset from podcasts
Mar 16, 2026Frederico Santos de Oliveira, Lucas Rafael Stefanel Gris, Alef Iury Siqueira Ferreira +8Automatic Speech Recognition Low-Resource Speech Recognition
Federal University of Mato Grosso (UFMT) · Federal University of Goias (UFG) · Paulista State University (UNESP) +2
Ramsa: A Large Sociolinguistically Rich Emirati Arabic Speech Corpus for ASR and TTS
Mar 9, 2026Rania Al-SabbaghSpeech Corpus Construction Arabic NLP
Department of Foreign Languages, University of Sharjah, UAE
Bolbosh: Script-Aware Flow Matching for Kashmiri Text-to-Speech
Mar 8, 2026Tajamul Ashraf, Burhaan Rasheed Zargar, Saeed Abdul Muizz +5TTS Synthesis Low-Resource TTS Synthesis
King Abdullah University of Science and Technology (KAUST) · Gaash Lab, National Institute of Technology Srinagar · Department of Linguistics, University of Kashmir.
ZeSTA: Zero-Shot TTS Augmentation with Domain-Conditioned Training for Data-Efficient Personalized Speech Synthesis
Mar 4, 2026Youngwon Choi, Jinwoo Oh, Hwayeon Kim +1Personalized Speech Recognition TTS Synthesis
Maum AI Inc., Republic of Korea · Humelo Inc., Republic of Korea
More Data, Fewer Diacritics: Scaling Arabic TTS
Mar 2, 2026Ahmed Musleh, Yifan Zhang, Kareem DarwishTTS Synthesis Low-Resource TTS Synthesis
QCRI, HBKU, Qatar

Oct 19, 2026J/K move · Enter open · S save

SEAM: Spectral Embedding Alignment and Merging for Low-Resource TTS

Walaa Othman, Baher Mohammad, Stamatios Lefkimmiatis, Ammar Ali

ITMO University · SPC RAS · MWS AI

Text-to-Speech Low-Resource TTS Synthesis Multilingual TTS Synthesis

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Expanding the vocabulary of conditional text-to-speech (TTS) models typically requires extensive fine-tuning with paired audio, creating a major bottleneck for low-resource and cross-lingual adaptation. To address this, we introduce SEAM, a zero-audio initialization method that constructs embeddings for unseen tokens from known subtokens. Unlike standard pooling strategies that ignore sequential structure, SEAM applies a spectral filter to preserve temporal phonetic order and uses closed-form ridge regression to align the new embeddings with the pretrained conditioning manifold. Across three languages and two TTS architectures, SEAM approaches or matches baseline performance using up to four times less training data, reducing sample complexity by 75% compared to random initialization and mean pooling. Our approach provides a lightweight, reproducible, and theoretically grounded solution for vocabulary expansion in low-resource speech synthesis.