Speech Recognition and Synthesis

Últimos artículos

• Decolonizing Linguistic Policies in Automated Speech Recognition: A Framework for Cross-Culturally Competent Speech AI

  Jay L. Cunningham, Mark Atta Mensah, Richard Martinez, Joao Vieira da Silva Neto, Efi Dawodu · 7 de agosto de 2026

  This paper focuses on automatic speech recognition (ASR) and ASR-mediated voice interfaces that shape access to public services, healthcare, and education. We argue that persistent failures for low-resource, Indigenous, and non-standard language varieties are not only technical errors, but also impl…

• PD-GS: Phoneme-Driven 3DGS for Audio-Driven Talking Heads

  Ao Fu, Yi Zhou · 7 de agosto de 2026

  3D Gaussian Splatting (3DGS) enables fast, photorealistic talking-head rendering, yet accurate lip articulation remains elusive: mouth motion is often over-smoothed and may violate hard articulatory constraints such as bilabial closures, producing the notorious ``leaky mouth'' artifact. A key diffic…

• LILAC: An Idempotent Neural Speech Codec

  June Young Yi, Dongwook Lee, Jiheum Yeom, Sungroh Yoon · 7 de agosto de 2026

  Neural Audio Codecs are widely adopted in speech generation and editing. However, existing neural audio codecs are not idempotent: across the paper's twelve baseline systems, every configuration tested rewrites, on average, at least 15% of its tokens in a single decode-re-encode pass. This poses a p…

• Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors

  Arya Labroo, Mengjie Qian, Kate Knill · 7 de agosto de 2026

  Automatic speaking assessment systems are increasingly deployed in high-stakes settings to mark second language (L2) learners' speaking tests, making it critical to show that their scores depend on speaking proficiency rather than irrelevant speaker attributes such as first language (L1) or age. Tra…

• Breaking the Curse ofMultilinguality inMany-to-Many Speech-to-Text Translation via a Resource-AwareMixture of Speech Encoders

  Yexing Du, Kaiyuan Liu, Youcheng Pan, Bo Yang, Chengpeng Fu, Yu Wang, Ming Liu · 6 de agosto de 2026

  Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT). However, when processing multilingual speech inputs, a single speech encoder shared across all languages suffers from the curse of multilinguality: languages at different resource levels …

• MERaLiON-GR: Speech Gender Recognition Model for English and SEA Languages

  Qiongqiong Wang, Ai Ti Aw, Nancy F. Chen, Ying Lay Chiu, Yang Ding, Yingxu He, Ridong Jiang, Zhuohan Liu, Yanfeng Lu, Yi Ma, Muhammad Huzaifah, Nabilah Binte Md Johan, Nattadaporn Lertcheva, Pham Minh Duc, Sailor Hardik Bhupendra, Siti Umairah Binte Mohammad Salleh, Shuo Sun, Tarun Kumar Vangani, Jeremy H. M. Wong, Jinyang Wu, Longyin Zhang · 6 de agosto de 2026

  We present MERaLiON-GR, a speech gender recognition system that performs binary classification (female / male) on English and Southeast Asian (SEA) languages. The model finetunes MERaLiON-SpeechEncoder-2, a large conformer based transformer pre-trained on a broad speech corpus, and applies parameter…

• Domain-Specific Evaluation of Text-to-Speech Systems: A Multi-Metric Benchmarking Study

  Ali Jafar, Amal Sarmad, Shifa Yousaf, Maryam Bashir · 4 de agosto de 2026

  Recent advances in neural text-to-speech (TTS) systems have substantially improved speech naturalness and intelligibility across many languages. However, comprehensive evaluation methodologies that jointly assess perceptual quality, speaker similarity, and acoustic fidelity across diverse speech dom…

• Latent Softmax for Data-Efficient Phoneme-Based Multilingual ASR Across Tonal and Non-Tonal Languages

  Saierdaer Yusuyin, Nanling Jiang, Hao Huang, Zhijian Ou · 4 de agosto de 2026

  Phoneme-based multilingual automatic speech recognition (ASR) can share acoustic evidence across languages more directly than language-specific subword modeling. When tonal and non-tonal languages are jointly trained, however, their supervision granularity does not match: tonal languages annotate to…

• SoniSpeech: A Large-Scale Open-Vocabulary Tri-Modal Dataset for Wearable Silent Speech Interfaces

  Ruidong Zhang, Jiacheng Liu, Fran\c{c}ois Guimbreti\`ere, Cheng Zhang · 4 de agosto de 2026

  Wearable silent speech interfaces (SSIs) are limited to small, closed vocabularies. Approaches achieving larger vocabularies require obtrusive hardware such as facial electrodes. We present SoniSpeech, the first large-scale, open-vocabulary, trimodal dataset for wearable SSI using acoustic-sensing e…

• REIMU: Efficient Heterogeneous Hierarchical Reasoning for SSL-Based Speech Deepfake Detection

  Kwok-Ho Ng, Tingting Song, Bingwen Feng, Peiya Li · 4 de agosto de 2026

  The increasing realism of speech generated by text-to-speech and voice conversion systems poses growing challenges to media integrity and voice authentication. Self-supervised learning (SSL) has substantially advanced speech deepfake detection, where downstream backbones conventionally process SSL r…

• Experience-Calibrated Contrastive Decoding for Mitigating Hallucinations in LM-Based Text-to-Speech

  Chenlin Liu, Minghui Fang, Zhonghao Bi, Zekai Su, Rong Wang, Jiqing Han · 4 de agosto de 2026

  Language model-based text-to-speech (LM-based TTS) remains vulnerable to speech hallucinations that deviate from the target text. Existing mitigation mainly relies on architectural changes or additional training, while decoding-time control remains underexplored. We present a conditional information…

• DLLM-TTS: Block Discrete Diffusion Language Model for Text-to-Speech Synthesis

  Wasim Madha, Nityanand Mathur, Hamees Sayed, Apoorv Singh, Sameer Khurana, Akshat Mandloi, Sudarshan Kamath · 4 de agosto de 2026

  Current text-to-speech systems face a trade-off: autoregres- sive codec language models produce highly intelligible speech but require large-scale models and training data and decode tokens sequentially, while non-autoregressive approaches im- prove speed at the cost of linguistic accuracy. We prese…

• Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens

  Yi Luo, Rongzhi Gu, Jixun Yao · 3 de agosto de 2026

  Balancing sequence length, representational capacity, and long-horizon stability is a central problem in autoregressive (AR) speech and audio generation. Representations with higher frame rates or greater capacity can preserve more signal detail, but they also make streaming generation more vulnerab…

• Voice Memory for Agentic Speech Recognition

  Chao-Han Huck Yang, Zih-Ching Chen, Piotr Zelasko, Zhehuai Chen, Jagadeesh Balam, Boris Ginsburg · 30 de julio de 2026

  We present Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory.md and decides per utterance whether to act on the hypothesis or abstain and keep the 1-best. Asynchronously, a score-gated optimizer revises that file…

• A large-scale corpus of religious radio broadcast transcripts from webstream recordings in the United States

  Samuel Bestvater, Athena Chapekis, Skyler Seets, Anna Lieb, Sono Shah, Aaron Smith · 30 de julio de 2026

  Religious radio is a widespread but understudied form of mass communication in the United States, and content-level analysis of it has been constrained by the absence of large-scale transcript data. This Data Descriptor presents a corpus of transcribed English-language religious radio broadcasts cap…

• VAD to the Bone: Ultra-Tiny Speech Activity Detection for Edge Deployment

  Stephen Bauer, Sheila Seidel, Shanza Iftikhar, Scott Veidenheimer, Gorkem Ulkar · 29 de julio de 2026

  Voice activity detection (VAD) triggers downstream speech processing in always-on systems under strict memory, latency, and compute constraints. Recent compact models report strong accuracy but rely on components that are not widely supported: learnable filterbanks, recurrent layers, or non-causal p…

• Analyzing the Importance of Blank for CTC-Based Knowledge Distillation

  Benedikt Hilmes, Nick Rossenbach, Ralf Schl\"uter · 28 de julio de 2026

  With the rise of large pre-trained foundation models for automatic speech recognition new challenges appear. While the performance of these models is good, runtime and cost of inference increases. One approach to make use of their strength while retaining efficiency is to distill their knowledge to …

• Unified Static-Dynamic Pruning for Efficient LLM Inference

  Jinhyeok Kim, Yejoon Lee, Jaeyoung Do · 27 de julio de 2026

  The increasing deployment of large language models (LLMs) has magnified the computational and memory bottlenecks of autoregressive decoding, where low compute intensity and bandwidth-bound kernels dominate inference cost. Weight pruning offers a promising remedy, but existing methods remain confin…

• MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond

  Lorenzo Concina, Seraphina Fong, Marco Matassoni, Alessio Brutti · 27 de julio de 2026

  Lightweight projectors are an established way to connect pre-trained speech encoders with large language models (LLMs), mapping acoustic features into token-level embeddings for tasks like ASR and spoken question answering. Existing systems, however, typically only support a few languages and are of…

• Synthetic Speech, Real Signal: Paralinguistic Preservation and Cross-Lingual Augmentation via Voice Cloning

  Roseline Polle, Owen Parsons, George Fairs, Luis Miguel San Martin Fernandez, Cole Looney, Xiaoliang Wu, Alexandra Livia Georgescu, Stefano Goria · 27 de julio de 2026

  Synthetic data augmentation in speech is common practice for linguistic tasks like ASR, but has seen far less work for paralinguistic ones, especially clinical tasks where labelled data is expensive and some patient groups are underrepresented. Voice cloning is one such augmentation approach, but is…

• Probing Speaker Identity Sensitivity in Audio Deepfake Detectors

  Daniyal Kabir Dar, Arun Ross · 27 de julio de 2026

  Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks. Yet the same detector that achieves less than 1% error on one dataset can see its error rate increase twentyfold when evaluated on a different dataset. We argue tha…

• Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin

  Zhiheng Qian, Aini Li, Hai Hu, Liang Zhao · 24 de julio de 2026

  Phonetic forced alignment is a key technique in phonetic research, yet existing alignment systems lack specialized models for low-resource language varieties. We address this by training text-dependent and text-independent aligners for Chengdu Mandarin using a 17-hour corpus and a custom G2P diction…

• Faster IndexTTS-2: Accelerating and Streaming Autoregressive Zero-Shot Text-to-Speech Synthesis on GPUs

  Muyang Du, Shuang Yu, Junjie Lai · 24 de julio de 2026

  Autoregressive text-to-speech models achieve strong naturalness but suffer from slow inference due to sequential token generation, limiting their deployment in production applications that require low latency. IndexTTS-2 is a state-of-the-art autoregressive TTS model consisting of a GPT, a flow-matc…

• Cumsum-Composable Phase Transport for Low-Cost Streaming Keyword Spotting

  Mahesh Godavarti · 23 de julio de 2026

  State-space sequence models are attractive for streaming speech because they maintain compact recurrent state, but scan-style training kernels can have unfavorable constants for short audio tasks. We study cumsum-composable phase transport, a streaming-native temporal layer for keyword spotting. Eac…

• ChipChat: Low-Latency Cascaded Conversational Agent in MLX

  Tatiana Likhomanenko, Richard He Bai, Zijin Gu, Zakaria Aldeneh, Shiladitya Dutta, Luke Carlson, Han Tran, Yizhe Zhang, Ruixiang Zhang, Huangjie Zheng, Navdeep Jaitly · 21 de julio de 2026

  The emergence of large language models (LLMs) has transformed spoken dialog systems, yet the optimal architecture for real-time on-device voice agents remains an open question. While end-to-end approaches promise theoretical advantages, cascaded systems (CSs) continue to outperform them in language …