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All fields3318Multimodal499Agents497Frontier LLMs430Inference & serving247Learning verifiable rewards165Image segmentation164Graph learning1583D scene142Evals108Voice AI88Classifier & causal discovery77Flow matching76Forecasting time75Training & fine-tuning64Teacher policy distillation56All 32 fields
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Papers

  • arXiv cs.CLOct 19

    AfriBED: a Speech Recognition Bias Evaluation Dataset for Low Resource African Languages

    Claytone Sikasote, Hussein Suleman, Jan Buys

    Despite advancements in automatic speech recognition systems (ASR), current systems exhibit speaker-attribute bias, i.e., their speech recognition performance is biased against certain speakers based on their demographic attributes such as gender, accents, and speaker type. However, accurately assessing speaker attribute bias in a given ASR system requires the availability of a bias evaluation dataset with comparable speech across subgroups of a speaker attribute. In this paper, we introduce African speech Bias Evaluation Dataset (AfriBED), a novel speech recognition bias evaluation dataset for two low resource African languages, Bemba and Nyanja, to detect and estimate gender and speaker-type bias for ASR systems. The dataset comprises 9 hours of speech with 3928 utterances recorded by 80 speakers for Bemba, while the Nyanja set includes 6260 utterances with approximately 18 hours of speech recorded by 127 speakers. The results of our baseline experiments demonstrate that the datasets are useful for examining gender and speaker-type attribute bias in the target languages.

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  • arXiv cs.CLOct 19

    Monsoon: Scalable Spontaneous Speech Collection for Low-Resource ASR in 50 Languages

    Shobhit Banga, Manas Dhir, Aaditya Pareek, Amritansh Walecha, Bhaskar Singh +6

    For most languages, automatic speech recognition (ASR) is limited less by model architecture than by a lack of accurately transcribed spontaneous speech. We present Monsoon, a framework for collecting such data at scale. It elicits unscripted conversations between paired contributors, screens recordings with automated classifiers, corrects transcripts over five levels of human review, and selects segments for lexical and acoustic coverage. It has collected speech in 50 languages; we analyse seven 3,500-hour corpora. At equal volume, its quality stages reduce semantic word error rate (WER) by 10-39% relative to a conventional protocol. Compared with an architecture-matched baseline trained on public corpora, Whisper fine-tuned on Monsoon performs comparably on the baseline's own test sets and reduces semantic WER by 20-44 points on conversational benchmarks. The gains transfer to a second architecture and grow with data volume. We release the framework, its quality-control models, and the fine-tuned models.

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  • arXiv cs.CVOct 19

    From Short Clips to Real-Time Streams: Identity-Aware Supervision and Context-Efficient Generation for Video Face Swapping

    Zekai Luo, Yuwei Liang, Ruiqi Ma, Haoyu Huang, Xuehao Wang +6

    Video face swapping transfers the reference identity to a target video while preserving its other attributes and motion. Despite recent progress in reference-guided flow-based methods, jointly achieving faithful identity transfer, high fidelity, and real-time generation over long video streams remains challenging. To address this challenge, we rethink the task-specific structure of reference-guided video face swapping. First, face swapping changes only identity while preserving most visual content, yet flow-matching supervision is weakly sensitive to these changes. We therefore develop Joint Rec-ID Loss, which balances explicit identity supervision and latent reconstruction according to the noise level. Second, the target video already provides non-edited attributes and temporal dynamics, enabling generation with fewer spatial tokens and shorter temporal contexts through Face-RoI Generation and Short-Context Rollout Training. By integrating these designs, we propose StreamingSwap, a reference-guided flow-matching framework for real-time streaming video face swapping. We also introduce HumanFaceVid, comprising 1,000 hours of cinematic training data and evaluation sets of 200 short…

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  • arXiv cs.CLOct 19

    Conducting Reliable Keyword Estimation with Unreliable ASR in Police Audio Data

    Robert Cole Molloy, Rhiannon N. Miller, Stephen Morgan, Anjalie Field

    Extracting insights from large audio data sets using human transcription is expensive and time-consuming. Off-the-shelf Automatic Speech Recognition (ASR) systems offer an easy-to-use scalable alternative, but systematic transcription errors, especially in domain-specific settings, can bias downstream analyses and lead to unreliable conclusions. We investigate whether imperfect ASR transcripts can still be used to obtain statistically valid keyword frequency estimates by combining model outputs with a small set of human-labeled examples, following Prediction Powered Inference and Design-based Supervised Learning frameworks. We evaluate this approach on police radio communications from Baltimore and Chicago, a domain in which keyword distributions could provide valuable insights into police activity, but off-the-shelf ASR systems exhibit systematic errors on operationally important terminology. Using bootstrap-based confidence interval coverage as our evaluation metric, we find that our approach substantially increases the number of domain-specific keywords that can be analyzed with statistically valid confidence intervals, from 43.0% using ASR alone and 27.2% using only a small…

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  • arXiv cs.CLOct 19

    TeleASR-Amharic: A Benchmark for Robust Amharic ASR in Realistic Telecommunication Conditions

    Dagnachew Mekonnen Marilign, Enrique Molero, Abrham Haile, Louis Powell, Israel Abebe Azime

    The real-world usability of ASR models remains challenging, particularly for low-resource languages, where existing speech resources are often collected in clean studio environments. In this work, we introduce TeleASR-Amharic, a benchmark for evaluating Amharic ASR under various acoustic and telecommunication conditions. We analyze the major degradations observed in real telecommunications recordings and develop a benchmark that covers multiple channel conditions, environmental disturbances, and telecom-domain variations. Our results show that telecommunication degradation can substantially reduce recognition performance, making even high-performing ASR models impractical under severe conditions. For example, the WER of the best-performing model increases from 0.23 to 0.79 under severe telecommunication degradation, corresponding to a relative increase of 243% in recognition errors. We further investigate the effects of different noise and channel distortions and show that scaling training data and applying carefully designed telecommunication-aware augmentation substantially improve robustness. In particular, our best training configuration achieves a 23.6% relative WER…

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  • arXiv cs.CLOct 19

    Expert L2 English Speech Evaluation on a Gaming PC: Accuracy, Fluency, and Prosody with XLS-R, XEUS, and PhoneticXeus

    Richard Rose

    oollim is a locally executable system for evaluating L2 English pronunciation accuracy, fluency, and prosody on a consumer computer. The system combines pretrained XLS-R, XEUS, and PhoneticXeus representations with reference-text features and ridge regressions trained on SpeechOcean762, the Korean Learners' L2 Speech Corpus (English), J-AESOP, and their combinations. On 305 independently held-out recordings assessed by three expert raters, several ridge configurations substantially outperform archived Azure API scores obtained in January 2026 in both Spearman correlation and quadratic weighted kappa across all three constructs. These results support expert speech evaluation on consumer hardware using pretrained representations and construct-specific regressions, without neural-model fine-tuning or calibration on the private evaluation data.

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  • arXiv cs.SDOct 19

    AudioScorer: Learning Question-Aware Token Relevance for Large Audio Language Models

    Marwan Farag, Bin Yang

    Large Audio Language Models (LALMs) reason over speech, environmental sounds, music, and other acoustic information. However, their audio encoders produce dense token sequences that make long-form audio costly to process. Existing audio pruning methods reduce this cost using heuristics such as attention, representation similarity, or temporal sampling, but do not explicitly learn which tokens are most useful for a given question. We propose AudioScorer, a lightweight model that scores audio tokens according to the user question. It is trained by distilling the ranking of audio tokens under the frozen LALM's answer-gradient saliency, together with downstream answer supervision. Compared with other pruning methods, AudioScorer achieves the best or tied-best result at 10% token retention on 7 of 10 semantic and acoustic tasks, including speech recognition, emotion recognition, and audio-scene classification.

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  • arXiv cs.LGOct 19

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

    Walaa Othman, Baher Mohammad, Stamatios Lefkimmiatis, Ammar Ali

    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.

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  • arXiv cs.CLOct 19

    Heartbeat Thinking: Speculative Reasoning and Tool Execution While Listening in Realtime Voice Agents

    Zhenyu Zhang, Kewei Fu, Yongqi Tong, Chenzhi Zheng, Wei Wu +2

    Tool-using voice agents often leave users waiting after speech ends, even when actionable information arrives earlier in the request. Speculative approaches begin work during speech, but chunk-bounded or interrupt-on-update generation can truncate a tool call before its arguments are complete. This couples computation time to speech updates, limiting useful work completed in advance. We introduce Heartbeat Thinking, which handles updated input through new Thinker invocations while allowing ongoing invocations and dispatched tools to finish. Periodic invocations read the latest partial transcript and a shared record of completed reasoning and tool results. These results help resolve missing arguments and initiate dependent calls, allowing preparation to progress across speech updates. Across SpeechFCEval, FDBench v3, and AudioCRAG, Heartbeat improves aggregate speculative tool coverage or retrieval reuse over a SHANKS-style windowed baseline, with benchmark-dependent effects on final task quality. On FDBench v3, it increases tool coverage by 16 percentage points and reduces mean final-response latency by 36.4\%. The results show that heartbeat thinking paradigm preserving ongoing…

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  • arXiv cs.CLOct 19

    VISAGENT: An Agent-Based End-to-End Voice Interaction System

    Xiang Wang, Zhaoxiang Liu, Xiang Chen, Liqiang He, Kai Wang +1

    Voice-based public services have evolved from manual switchboards to intelligent platforms to meet modern user needs. However, current systems remain constrained by rigid menu-driven interaction, fragmented vertical services, and limited extensibility. To address these issues, we present VISAGENT, an agent-based end-to-end voice interaction system for large-scale public services that integrates end-to-end speech interaction, hierarchical agent routing, a scalable voice-agent marketplace, and user memory support. The system replaces menu-driven workflows with natural speech and dynamically dispatches user requests to either a general conversational model or specialized domain agents via a two-level routing paradigm that achieves sub-second intent-to-agent selection over hundreds of agents. User memory informs preference-aware agent selection and supplies relevant preferences and context to selected agents, supporting personalized task execution and reducing the need for users to repeat previously provided information. A voice-agent registration mechanism converts task agents into conversational entities, enabling dynamic onboarding and ecosystem-level extensibility. We validate…

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  • arXiv cs.SDOct 19

    Multilingual Voicemail and Answering Machine Detection via Pretrained Audio Embeddings and Gradient Boosting Machines

    Kemal Altwlkany, Muamer Paric

    Answering machine detection (AMD) is a fundamental task in telephony that classifies whether an answered call is picked up by a human or a voicemail (machine). Despite its widespread use across cloud communication platforms, existing approaches rely on proprietary systems with undisclosed architectures, or require computationally expensive models that cannot operate in real time. We propose a lightweight AMD system that combines pretrained audio embeddings with gradient boosting machines, trained to prioritize the correct detection of human-answered calls. We evaluate seven models across two datasets: three models that we propose, a proprietary industry model, two prior academic approaches, and a large language model baseline that relies on automatic speech recognition. Furthermore, we release OpenAMD, the first publicly available AMD dataset comprising of more than 500 recordings in 7 languages. Our best model achieves the highest precision across both datasets while maintaining low inference latency. We assess the robustness of models to additive noise and packet loss, and perform tests of statistical significance to discover per-language and per-gender model performance.…

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  • arXiv cs.CLOct 19

    Speech LLMs That Breathe: Modeling Non-Verbal Vocalizations in Dialogue

    Heyang Liu, Lixin Zhang, Jiayi Huang, Ziyang Cheng, Yuhao Wang +4

    The user experience of speech interaction models has improved, benefiting from the modeling of paralinguistic attributes and advances in speech reconstruction paradigms. However, human communication follows extensive norms regarding non-verbal vocalizations (NVVs) spanning respiratory, affective, and discourse. The omission of these events imposes inherent limitations on the naturalness of current approaches. In this work, we cover 13 major types of NVVs and construct a natural dialogue dataset that conforms to human conversational norms through an automated synthesis pipeline. We enhance the ability of speech interaction models to generate contextually appropriate NVV events through multi-stage fine-tuning and NVV-aware GRPO (Group Relative Policy Optimization). Based on distinct modeling paradigms, we substantially improve the expressiveness of VocalNet and VITA-Qinyu in empathy interactions and role-play dialogues, and further discuss the architectural necessity for achieving high-quality natural speech generation.

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  • arXiv cs.SDOct 19

    Manifold-Constrained Residual Adaptation with Frame-Adaptive Layer Aggregation for Speaker Verification

    Zezhong Jin, Xiaoyu Wang, Zhe Li, Chong-Xin Gan, Siqing Qin +2

    Large self-supervised speech models provide powerful representations for speaker verification, but full fine-tuning is expensive. Most conventional adapters insert small trainable modules to modify the output of each layer, while keeping the original residual connections unchanged. Building on our preliminary study of manifold-constrained Hyper-Connections in speaker encoders, we explore parameter-efficient adaptation of both residual paths and feature selection across layers. We first propose a Manifold-Constrained Residual Adapter (MCRA), which splits the hidden dimensions of each Transformer residual state into N groups and uses a small Sinkhorn-normalized matrix to exchange information among them. A learnable gate controls the balance between the original and mixed residual paths. The gate adds only one scalar parameter at each residual connection and initially favors the original path, allowing gradual adaptation. We further propose Frame-Adaptive Layer Aggregation (FALA), an attention-based aggregation method that uses a single learned query shared across all utterances to compute layer weights separately for each speech frame. Because the layer representations depend on…

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  • arXiv cs.LGOct 19

    Text Dominance in Paralinguistic Conflict: Tracing Suppression Through Audio LLM Circuits

    Ankur Banerjee, Vishnu Guntakala, Danity Pike, Advay Roongta, Kevin Yang +1

    Audio LLMs deployed in settings such as crisis hotlines and customer support are only useful beyond speech-to-text pipelines if they use acoustic cues rather than the transcript alone. We test whether they prioritise transcript content over paralinguistic cues (emotion, speaker identity, sarcasm) and trace the mechanism when they do. Across six audio LLMs under LISTEN-style conflict conditions, the mean text-dominance rate on mismatched text+audio items is 0.78 (0.86 excluding sarcasm). In the most text-dominant screened model, probing shows speaker identity remains linearly decodable at ceiling at the language model's input, so the failure is not perceptual. Back-patching, reported to repair lexical text dominance, has no measurable effect here; but it also fails on its own source task in our replication (n=300), so we treat its null as uninformative rather than as evidence of non-transfer. Attention knockout partially restores audio-grounded output by blocking the claim from propagating through intermediate tokens during open-ended generation, whereas blocking the answer position's direct connection to the conflicting claim has no effect. The claim's influence therefore travels…

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  • arXiv cs.CLOct 18

    Quality-Controlled Reply and Summary Data for Post-Training Small Language Models in AI Call-Screening Agents

    Jiaqi Gan, Tsang Ng, Ankit Raj, Tommy Duong, Xingyu Shen +3

    Small language models have improved to the point where a phone can plausibly screen its own calls, answering on an absent owner's behalf without sending call content off the device, a privacy-respecting alternative to cloud call handling. Realizing that promise takes a model that can do two things well: reply within the owner's authority during the call, and afterward tell the owner what happened and what needs attention. Released small models do neither reliably - they raise unwarranted alarms about ordinary callers and write summaries that drop material conditions or report a caller's claims as fact - and real calls supply no reference replies or summaries to learn from. We construct supervision for both tasks from synthetic calls under an information-boundary constraint: a caller writer works from a private scenario brief, a reply writer sees only the conversation up to the current turn, and a summary writer sees the completed transcript. Separate model-based reviews of the full dialogue and the summary decide which calls enter training, yielding a corpus of 3,395 calls with 15,102 targets (11,707 reply, 3,395 summary). Training a single shared LoRA adapter on both tasks, we…

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  • arXiv cs.SDOct 18

    Whistle and Wrong Lyrics: Synthetic Queries Are Not Enough for Real-World Query-by-Melody

    Luigi Rivolta

    People look up a song by humming it, but also by whistling it or singing it with the wrong words. Published query-by-humming systems and benchmarks focus on hums and sung queries, and no public dataset pairs whistles or wrong-lyrics singing with song recordings. We study both query types with a melody-only retrieval system: a key-normalized pitch track embedded by a 5.2M-parameter contour encoder, with a spectral-peak tracker for whistles. First, a physics-grounded generator renders synthetic whistles and nonsense-syllable singing from the vocal melodies of 11,083 songs. Training on them raises top-1 retrieval of held-out synthetic queries against an 11,050-song gallery from 0.180 to 0.251 for whistles and from 0.193 to 0.264 for wrong-lyrics singing, but the gain does not transfer: on the MLEnd test performers the best synthetic-only model gets 210 of 339 real whistles right at top-1, against 214 for the baseline. Second, a closed-set fine-tune on real MLEnd whistles with performer-disjoint splits raises whistle-to-hum top-1 from 0.631 (95% CI 0.581-0.681) to 0.673 (0.622-0.723), on known songs only. Third, two further generations of synthetic queries also fail to transfer:…

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  • arXiv cs.CVOct 18

    CASE: Complementary Articulation-Aware Speech Encoder to Enhance Speech-Driven 3D Facial Animation

    Jaehong Myung, Seungho Eum, Taegon Kim, Hyunjung Chung, Unsang Park

    General-purpose self-supervised speech encoders provide strong acoustic representations, but their pretraining objectives are not explicitly aligned with the visible articulatory dynamics required by speech-driven 3D facial animation. We propose CASE (Complementary Articulation-aware Speech Encoder), a complementary speech encoder adapted by contrastively aligning short speech clips with continuous 3D lip-mesh VQ-VAE latents. After adaptation, the geometry branch is removed, and the audio-only CASE representation is fused with the baseline SSL speech representation through residual cross-attention, while retaining the motion-decoder architecture and objective. Across four VOCASET baselines, CASE reduces mean lip vertex error by 2.8-6.0%, and mean full-face vertex error decreases in every system. Under the same fusion protocol, CASE yields lower mean LVE than the unadapted AV-SE stream on all four generators, with model-dependent margins. Because adaptation uses Korean speech-geometry pairs and evaluation uses English VOCASET, the result is consistent with cross-language downstream transfer under a shared English-pretrained audio-visual prior.

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  • arXiv cs.CVOct 18

    Stable Signer V3: 4D Deformable Primitive Splatting Is All You Need for Sign Language Production

    Sen Fang, Liwei Che, Dimitris N. Metaxas

    We present Stable Signer V3, an interactive high-resolution sign language avatar system built on 4D Deformable Primitive Splatting. It addresses three limitations of existing avatars: restricted facial expression, inaccurate identity-specific body deformation, and loss of fine appearance details. Shared Face Primitive Splatting adds transferable superquadric primitives for richer expression control; Test-Time Body Primitive Splatting learns an identity-specific virtual muscle layer for more faithful motion; and Test-Time Tunneling Super-Resolution uses TunnelingNet to recover identity-specific high-resolution details. Both test-time modules are optimized once per signer identity; inference then runs entirely from the stored identity parameters. Together, they reduce facial geometry error by 71.4%, body-boundary error by 35.1% across ten identities, and high-resolution RGB reconstruction error by 24.2% on held-out evaluations, while retaining interactive streaming.

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  • arXiv cs.SDOct 18

    DF-PALM: Dynamic Fusion Prompt Learning for Audio--Language Models

    Yulin Sun, Shenghua Li, Yong Dou, Xinwang Liu, Cheng Yang +1

    Few-shot audio classification requires audio--language models to distinguish variable acoustic events from limited labeled data. PALM's main formulation adapts encoded class representations without changing pretrained encoders, but its class representations remain input-independent and classification uses audio features without class-conditioned refinement. We introduce DF-PALM, a dynamic feature-space prompting framework that combines audio-conditioned class representations with asymmetric cross-attention for audio refinement. Class representations are conditioned after text encoding, held fixed across fusion layers for each input, and reused for final scoring. Both pretrained encoders remain frozen, avoiding audio-conditioned text-encoder passes. With PENGI as the frozen backbone, the main 16-shot evaluation covered 11 benchmarks and six baselines. DF-PALM achieved 80.69% mean accuracy and 80.32% macro-averaged F1, exceeding PALM by 4.11 and 4.30 percentage points, respectively. Paired benchmark-level tests supported both gains after Holm correction. Across four shot settings, DF-PALM achieved the highest mean accuracy and macro-averaged F1 among the four evaluated…

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  • arXiv cs.CVOct 17

    Predicting Individual Differences in Lipreading From Eye-Tracking and Pupillometry During Silent Visual Speech

    Puneet Kumar, Satyarth Malik, Gao Zheng, Guoying Zhao, Xiaobai Li

    Lipreading ability varies widely, including among people who are hard of hearing. Although mouth-directed attention is linked to speechreading outcomes, it remains unclear how much lipreading skill can be inferred from viewing behavior and which signals carry this information. We examine whether individual differences in lipreading ability are reflected in viewing behavior during silent speech and quantify the contributions of gaze dynamics, pupil-linked signals, and spatial viewing allocation. We introduce the Lipreading Perception Study (LiPS) dataset and a tracker-independent preprocessing pipeline that synchronizes SR Research EyeLink gaze and pupil recordings with the stimuli. Twenty-four adults (age 32 $\pm$ 10 years) completed a four-alternative forced-choice silent lipreading task. An extreme-groups protocol yielded 18 participants labeled as stronger or weaker lipreaders. Each participant-trial included four aligned views: lip-region images, frame-level gaze, pupil summaries, and spatial viewing proportions. A four-branch neural network with cross-modal attention predicted labels using participant-disjoint splits and systematic ablations. The full model reached accuracy…

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Research · AI field

Voice AI

Speech recognition, speech synthesis, voice agents, spoken dialogue and audio language models.

Field page: people, tools, topics →
88papers this week−25.4% vs last week553 this month

Open problems in Voice AI

  • Spatial audio alignment in joint video-audio generation3 papers raise it · 3 address it

    Immersive applications like AR/VR require stereo audio that dynamically matches visual source motion, which current models often lack.

    • Hear the World in Stereo: Learning Dynamic Spatial Correspondence for Immersive Joint Video-Audio Generationraises · addresses
    • WorldSonus: Bringing Sound to Worldsraises · addresses
    • HelixWorld: A Real-time Interactive Audio-Visual World Modelraises · addresses
  • Real-time joint audio-visual generation2 papers raise it · 2 address it

    Interactive applications need low-latency synthesis to keep pace with user input and camera motion.

    • WorldSonus: Bringing Sound to Worldsraises · addresses
    • HelixWorld: A Real-time Interactive Audio-Visual World Modelraises · addresses
  • Multi-stem audio separation and generation1 paper raises it · 1 addresses it

    Rendering dynamic scenes requires individual control over sound sources, but most models output only mixed audio.

    • SepGen: Multi-Stem Audio-Video Separation and Generation in a Single Modelraises · addresses
  • Event-level audio-video consistency evaluation1 paper raises it · 1 addresses it

    Complex scenes with multi-event composition and asymmetric cross-modal relations need fine-grained metrics beyond whole-clip assessments.

    • AVC-Reward: Event-Centric Audio-Video Consistency Evaluationraises · addresses
  • Accent leakage in cross-lingual voice cloning1 paper raises it · 1 addresses it

    Zero-shot cross-lingual TTS often inherits the reference speaker's accent, degrading naturalness in the target language.

    • Accent Analogy Guidance: More Speaker Similarity at Equal Accent in Cross-Lingual Voice Cloningraises · addresses

Named by an AI from these papers' abstracts.