arXiv_SD
-
Backward Compatibility in Zero-Bit Neural Audio Watermarking
2026-10-15 17:45:27Admir Krni\'c, Kosta Pavlovi\'c, Petar Nedi\'c, \v{Z}eljko Bolevi\'c, Slavko Kova\v{c}evi\'c, Igor Djurovi\'cAbstract
Neural watermarking systems are typically trained as tightly coupled embedder-detector pairs and implicitly assume static deployment. In real-world deployments, embedders and detectors are frequently updated and selectively decoupled for security and versioning purposes, which can break detection in previously embedded content and, more critically, increase false-positive rates on embedders that a given detector is intentionally not intended to recognize. In this paper the concept of backward compatibility in zero-bit audio watermarking is formalized. A training framework is proposed that pairs a new detector with a sequence of frozen legacy embedders (its legacy chain), using a performance-weighted sampling strategy so that the detector retains accuracy across all earlier versions. This coupling, while necessary for backward compatibility, is shown to induce an embedder-collapse effect that catastrophically degrades the true negative rate (TNR) against out-of-chain embedders. To counteract this, a Angular Diversity Loss (ADL) is introduced that explicitly forces each new embedder to occupy a distinct region of the output space. The combined approach restores near-perfect TNR with no measurable cost to detection accuracy or perceptual audio quality.
Abstract (translated)
URL
https://arxiv.org/abs/2610.22296
PDF
-
From Signals to Evidence: Interpretable Underwater Acoustic Target Sensing with Large Language Models - Part I: Framework and Recognition
2026-10-15 15:27:49Yuge Zhang, Jie Chen, Zhengqiao Zhao, Xiling Yao, Lingji Xu, Jingdong Chen, Yixin YangAbstract
Underwater acoustic target detection and recognition are essential to marine surveillance and intelligent ocean sensing. Despite substantial advances in deep learning, existing systems typically produce binary detection decisions, categorical labels, or scalar confidence scores, offering limited insight into the physical evidence underlying their outputs. Large language models (LLMs) offer an opportunity to move underwater acoustic sensing toward interpretable, evidence-grounded understanding; however, generic audio encoders struggle to capture the distinctive physical characteristics of underwater acoustics, and physically annotated data are scarce. In this paper, the first of a two-part series, we propose a from-signals-to-evidence framework that integrates physics-aware acoustic encoding with a pretrained LLM, organized around a unified feature-evidence-decision chain with a shared reasoning core. An open input layer accommodates heterogeneous information-raw waveforms, time-frequency representations, environmental and geographic data, and textual physical priors-unifying detection and recognition within a common formulation. In Part I, we instantiate this framework for target recognition through an Underwater Acoustic Physics-aware Transformer (UPT), which tokenizes complementary acoustic representations and learns task-disentangled physical representations through four physics-oriented learning tasks. We develop a physics-parameterized data engine that provides traceable supervision at two levels of granularity and introduce an attribute-level evaluation protocol to assess the physical fidelity of generated descriptions. Experiments on simulated recordings of four vessels demonstrate accurate vessel recognition and reliable estimation of shaft frequency, blade-passing frequency, and machinery-related attributes.
Abstract (translated)
URL
https://arxiv.org/abs/2610.22064
PDF
-
Frame-Wise Streaming for Efficient Learned Speech Compression
2026-10-15 14:42:23Yuhan Liu, Jun Xu, Fengxi Zhang, Zhengxue Cheng, Li SongAbstract
Speech compression is increasingly important in scenarios such as real-time and satellite communication. Neural codecs have shown superior Rate-Distortion (RD) performance over traditional signal-processing codecs. Models with long-range dependency encoders/decoders and entropy models outperform convolution-based structures, but they still lack streaming support since they rely on sequence-level correlations. In this paper, we propose StreamLSC, a frame-level streaming learned speech compression model built on three fundamental components of streaming networks, i.e. Boundary-Cached Causal Convolution, Intra-Frame Layer Normalization, and RNN-mode RWKV. Our model incorporates a streamable entropy quantizer module, ensuring efficient context model while preserving causal constraints. Experiments show that the streaming model achieves performance comparable to its offline counterpart, attaining state-of-the-art rate-distortion results, significant gains in subjective evaluations, -51.37% (ViSQOL) and -53.99% (PESQ) BD-RATE savings on average, and improvements of 0.2154 and 0.5156 in BD-ViSQOL and BD-PESQ.
Abstract (translated)
URL
https://arxiv.org/abs/2610.21998
PDF
-
DuET: Note-Frame Dual Encoding for Piano Performance-to-Score Transcription
Abstract
Transcribing expressive piano performances into scores requires interpreting individual note timings within an underlying metrical structure. However, score attributes are associated with notes, whereas beats may occur without coincident note onsets. We introduce DuET, a Dual-Encoder Transformer that jointly learns note-level performance MIDI-to-score (PM2S) transcription and frame-level beat and downbeat tracking. Bidirectional cross-attention connects note and frame encoders, while an autoregressive decoder alternates between their representations to predict structured score attributes. We further introduce TimeRoPE and Time-Pitch Rotary Positional Encoding (TPRoPE) to establish a shared performance-time reference and explicitly encode relative temporal and pitch relationships. Evaluations on ASAP, a classical piano dataset, and our curated Gangqinpu dataset, which spans multiple genres with a focus on popular piano music, demonstrate improvements in score timing and notated duration. Compared with the strongest baseline on each dataset, DuET reduces MUSTER mean error from 6.56% to 3.64% on Gangqinpu and from 11.30% to 11.22% on ASAP. Ablations support the benefits of frame-level context, time-based positional encoding, and decoder-history masking. Code, weights, and demos are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.21467
PDF
-
Multiscale Generalized Sparse Plane Wave Decomposition in the Spherical Harmonic Domain
Abstract
Sparse plane wave decomposition (SPWD) is a physics-driven model-based approach for analyzing sound fields that is suitable for real-time operation. SPWD is used in different contexts such as direction of arrival (DOA) estimation and sound field separation. SPWD employs spherical Dirichlet kernels to decompose sound fields represented as steered response maps into a finite number of plane waves using sparse recovery techniques. This article proposes a multiscale generalized sparse plane wave decomposition approach that uses an optimal spherical kernel and a multiscale sparse decomposition method to improve recovery performance. The utility of the proposed approach is shown by comparisons with the original SPWD approach via extensive numerical simulations.
Abstract (translated)
URL
https://arxiv.org/abs/2610.21425
PDF
-
VoxTurn: Answering Several Turn-Taking Questions in One Pass with One Model
2026-10-15 03:06:58Yang XiaoAbstract
A cascaded voice agent must decide, throughout each silence of its user, whether the user has finished their turn: answering too early cuts the user off, answering too late makes the agent slow. It often needs related decisions too: will the user soon go on, is speech during its own turn an interruption or a backchannel, is a sound speech at all. With open detectors, each extra decision costs another model or another call, and on the 14-language eot-bench they cut users off in more than a quarter of mid-turn pauses. Because they are trained on segmented utterances, this mismatch is the largest single source of error. We present VoxTurn, a 0.8B speech LLM that casts these decisions as multiple-choice questions, scores the hidden state of each option, and answers any set of them in one forward pass with a block-isolating attention mask, exactly as if asked one at a time. Trained on real conversational pauses with labels derived from their timing, and re-queried while a silence lasts, VoxTurn reaches 11.5% false cut-offs in English at a 300 ms latency budget, comparable to two commercial detectors, and a 14-language mean of 19.9%, the lowest among open models, at 8.3 ms per decision. In Pipecat and LiveKit it cuts off 17% and 9% of turns, and it ranks fifth of 24 in TurnBench end-of-turn detection without any TurnBench training data. The model, code and framework plugins are released.
Abstract (translated)
URL
https://arxiv.org/abs/2610.21150
PDF
-
MIDI-REPA: Improving Text-to-Music Generation with Symbolic Representation Alignment
Abstract
State-of-the-art text-to-music models increasingly rely on scaling model capacity, data, and compute, while still learning musical structure directly from dense audio. Yet melody, rhythm, harmony, and structure can be represented compactly in symbolic form. We introduce MIDI-REPA, a representation-alignment framework that derives MIDI supervision from automatic music transcription (AMT) to align intermediate representations of a flow-matching DiT. Building on the 122M-parameter FluxAudio-S baseline from the 2026 ICME Academic Text-To-Music Challenge, we investigate what, where, and when to align. Aligning a MuScriptor-derived piano roll with an early DiT layer throughout the full flow trajectory performs the best. Without additional training data or techniques, MIDI-REPA remains competitive on the ATTM objective metrics while remaining trainable on a single RTX 3090 in about 31 hours. Our results highlight symbolic supervision as a simple and inexpensive learning signal for improving text-to-music generation. We release our transcription pipeline, JamendoMIDI, training code, and audio samples at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20967
PDF
-
Probing Neural Audio Codecs with Speaker Traits and Speech Characteristics
2026-10-14 21:27:37Jihwan Lee, Tiantian Feng, Minu Kim, Junhyeok Lee, Sean Foley, Thanathai Lertpetchpun, Najim Dehak, Dani Byrd, Shrikanth NarayananAbstract
Neural audio codecs are increasingly used for low-bitrate speech coding and serve as tokenizers for speech language models, yet it remains unclear how their codebook hierarchies encode speaker traits and speech characteristics. We systematically study 17 multi-codebook neural audio codecs across seven traits: sex, age, accent, voice quality, emotion, speech flow, and non-verbal sounds. For each codec, we reconstruct speech using partial codebooks and use machine listeners to compare trait predictions on each reconstruction. We measure prediction agreement with reweighted Cohen's \k{appa}, and define trait depth as a proxy for the codebook depth by which each trait is encoded and recoverable. Overall, sex has the shallowest trait depth, followed by speech flow, whereas voice quality and age group require deeper stacks; emotion, accent, and non-verbal sounds fall in between. Within each trait, speech-flow classes are at relatively similar trait depths, whereas emotion, accent, and non-verbal sounds differ more. Additionally, we explore how certain codec design choices relate to trait depths. These findings show that codebook hierarchies are trait-dependent and may not exhibit uniform coarse-to-fine structures, motivating trait-specific codec evaluation and design for future applications.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20955
PDF
-
SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References
2026-10-14 19:54:17Samuele Cornell, Matthew MaciejewskiAbstract
Unsupervised speech denoising methods such as MixIT and noisy-target training can be trained directly on noisy recordings, avoiding the sim-to-real gap of synthetic training data, but remain well below supervised performance. Teacher--student self-training, as in RemixIT, narrows this gap by remixing a teacher's estimates into new mixtures that supervise a student; however, since the student is trained only toward the teacher's estimates, teacher errors, in particular over-suppressed speech, are never recovered and propagate as the teacher is updated from the student. We propose SNOAR, a self-training framework that corrects the teacher's estimates before use: the noise leaked into its speech estimate is removed by differential noise filtering (DNF), and part of the noisy observation is added back in to restore suppressed speech and attenuate artifacts. We show that the two corrections depend on each other, as without DNF the loop collapses to the identity mapping. Compared to RemixIT, SNOAR is more stable along training and outperforms it in signal-based, perceptual, and word error rate metrics, reaching 97\% of the SI-SDR improvement of a fully-supervised topline on WHAM!; on real CHiME-3 recordings, it improves every metric of three different teachers, while RemixIT degrades their perceptual quality.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20863
PDF
-
Zero-Shot Spoken Language Understanding with SLAM-ASR: What Do Monolingual ASR-Trained Linear Projectors Enable?
2026-10-14 19:28:20Edwin Arkel Rios, Sneh Sourabh, Jocelyn Zaruma, Girish Ewoorkar, Julian Mack, Bo-Cheng LaiAbstract
The SLAM-ASR framework connects the features of a frozen speech encoder as input to a frozen LLM decoder to enable speech transcription. In this work we ask whether this training-on speech transcription-provides the LLM with the capability to also perform spoken language understanding (SLU) tasks, by training monolingual projectors on 11 languages. When prompted to answer a yes/no question about the input speech, the mean instruction following rate (IFR) is 49.8%. When not following the instruction, these models default back to transcription. The transcription quality-measured by the character error rate (CER)-is not sufficient to predict IFR. Remarkably, when the model is instructed to answer the topic question as a single digit option, the mean IFR rises to 81.3%. Our models score a mean 23.2% topic balanced accuracy (BAcc) against 14.3% chance. However, the same decoder scores a mean 71.5% BAcc when using reference transcripts as text input. This indicates a failure of the projector to transfer information for the SLU tasks.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20844
PDF
-
EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis
2026-10-14 18:39:51Parshav Singla, Simranjit Kaur, Maanya Walia, NiharikaAbstract
Understanding why listeners enjoy music is difficult because the experience is subjective and changes as a piece unfolds. Electroencephalography (EEG) offers a non-invasive way to study neural responses during listening, yet earlier work has mostly treated music emotion as a categorical label or relied on black-box models. We formulate enjoyment prediction as a regression problem on the public MUSIN-G dataset, which contains 128-channel EEG from 20 listeners together with song-level enjoyment ratings. Six deep learning models were compared under a participant-wise protocol, in which 15 listeners were used for training and validation and 5 were held out for testing. DeepConvNet, CNN-LSTM and GNN-CNN reached the lowest mean absolute error (0.97 to 0.99 on the 1 to 5 scale), while the temporal convolutional network (TCN) had the highest Pearson correlation with the ratings (r = 0.21). Gradient-based and occlusion-based explanations, averaged over all held-out windows, identify the time segments within each two-second window that most influence the TCN predictions. The study provides a reproducible cross-listener benchmark for EEG-based enjoyment regression and shows how explainability analysis complements standard metrics.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20795
PDF
-
Quantizer Redundancy and Exact Index Recoding in a Frozen Neural Audio Codec
Abstract
Residual scalar quantization can produce highly dependent indices when successive stages reuse identical, unscaled grids. This case study examines that behavior in a frozen neural audio codec and develops an exact recoding procedure that reduces storage while preserving its decoder inputs. Analysis of the repeated-grid recurrence identifies predictable stage sequences; finite-precision checks motivate preserving every original index through explicit exception records. The model weights and reconstruction graph remain fixed. On the studied 62- and 512-file cohorts, a predictor, adaptive coding and explicit file selection reduce complete stored bytes. The final 512-file rate is 129.591 kbps including headers, 25.943% below an original-index XZ control. Original recorded CPU runs establish bitwise latent and waveform preservation; independent readers verify exact index and metadata recovery in subsequent saved-stream experiments. Later coding choices use previously studied cohorts, and fresh encoding requires generating three alternatives. The engineering result is implementation-specific storage reduction with exact index recovery; the underlying lossy reconstruction quality remains unchanged.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20471
PDF
-
vst-bench-ml: Auditable Dataset Generation for Audio Plugins
Abstract
Generating datasets with audio plugins requires preserving how each audio observation was produced. vst-bench-ml is a Python package that samples plugin parameters, coordinates rendering through a separately installed host, and records software identities, rendering settings, and output hashes for later verification. A sealed manifest binds this evidence and supports offline integrity checks without loading the plugin. A companion repository provides a sample-free VST3 test plugin, a deterministic offline host, and additional checks linking rendering attempts to recorded outputs. On a pinned macOS arm64 and Python 3.12 stack, functional validation of the pre-release versions reproduced package and host tests. Two runs in fresh processes reproduced the same render-plan hash and all eight expected output hashes, covering four sampled parameter configurations and two fixed MIDI signals; 12 negative cases also passed. Later licensed 0.1.0 release candidates passed producer-run continuous integration and local validation. Version 0.1.1 adds public Git source repositories, package-index installation, and user documentation; a separate release check reproduced the expected plan and output hashes. These checks establish integrity and repeatability within the tested setup; they do not establish portability, scientific suitability, or perceptual quality.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20465
PDF
-
HRTF Upsampling and Personalization via Neural Operators
2026-10-14 16:03:02Meenakshi Krishnan, You Zhang, Ramani Duraiswami, C. Phillip BrownAbstract
Head-related transfer function (HRTF) spatial upsampling from sparse measurements is a fundamental challenge in personalized binaural audio. We formulate HRTF upsampling as an operator learning problem and introduce SPARO, a neural operator that maps sparse measurements at a set of spatial directions to the HRTF field, a continuous representation that can be queried at arbitrary positions. SPARO projects the measurements onto a learned set of spherical latent modes, processes the latent modes with Transformer blocks, and decodes the resulting representations at query directions. On SONICOM, using the LAP24 measurement configuration, SPARO attains the best log-spectral distortion (LSD) and interaural level difference (ILD) error at every sparsity level from 3 to 100 measurements (18% lower LSD and 43% lower ILD error than the strongest baseline at 100 measurements) while remaining competitive in interaural time difference (ITD) error. It reconstructs a dense HRTF 15 to 373 times faster than the leading neural-field method RANF. We further validate the approach on HUTUBS, demonstrating its effectiveness across HRTF datasets.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20400
PDF
-
MUSEBENCH: A Benchmark for AI-Generated Music Detection and Artifact Analysis
2026-10-14 14:57:38Yi Chen Liu, Syed Irfan Ali Meerza, Jiawei Yu, Xi Guo, Samuel Bright, Jian LiuAbstract
Advances in generative AI have made it possible to produce music that is nearly indistinguishable from human compositions, raising the question of whether such works can be reliably distinguished from human compositions. We present MUSEBENCH, a large-scale benchmark for AI-generated music detection and artifact analysis, comprising over 361K tracks. The dataset includes human compositions, stylistically matched music generated by ten state-of-the-art open-source and commercial models, such as Suno and Udio, as well as human-AI co-creations and variants processed with adversarial perturbations, compression, and acoustic post-processing. Using MUSEBENCH, we benchmark 11 automated detectors and evaluate their accuracy, generalization, and robustness, under default, out-of-domain, and transformed-variant experiment settings. We further conduct a comprehensive artifact analysis through acoustic feature inspection, identifying recurring signatures such as non-idiomatic harmonic progressions, upsampling induced spectral periodicities, and violations of physical performance constraints. Our findings reveal key strengths and vulnerabilities of existing detectors and provide practical insights for building more reliable AI music detection systems.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20310
PDF
-
Evaluating perceptual transparency of lossy music audio compression with Opus codec
Abstract
Although the open-source Opus audio codec is widely used for digital audio streaming, its perceptual transparency threshold for high-bitrate music compression remains insufficiently evaluated. To establish this threshold, this study evaluates Opus across a two-phase ITU-R BS.1116-3 double-blind framework, analyzing Difference Grades using a Linear Mixed-Effects Model (LMM) and Two One-Sided Tests (TOST) for statistical equivalence. In a first phase with 21 qualified listeners and 23 music excerpts, 128 kbps achieves statistical in-distinguishability ($p = 0.456$) but narrowly fails strict equivalence to the uncompressed reference ($p = 0.064$). In Phase 2, Best Linear Unbiased Predictors (BLUPs) isolate the 10 most critical listeners and 8 most challenging samples for a stress-test. Under these worst-case conditions, 128 kbps is conclusively detected as degraded ($p = 0.030$), whereas 160 kbps successfully reaches perceptual transparency ($p = 0.747$) and strict statistical equivalence ($p = 0.024$). The study concludes that 160 kbps demonstrates perceptual transparency to uncompressed CD quality in critical listening environments. For reproducibility, we publicly release results for both phases, anonymized subject background info and sample characteristics.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20246
PDF
-
Noise-Aware Lombard Speech Synthesis via Flow-GRPO for Intelligibility Optimization
2026-10-14 13:29:29Zhaoyang Wang, Dingding Yao, Chengzhong Wang, Junfeng LiAbstract
Voice-based conversational agents are increasingly deployed in real-world settings, where environmental noise often degrades the interactive experience. Therefore, robust acoustic adaptability is essential to maintain natural and intelligible human-computer communication. However, most existing systems remain acoustically insensitive and fail to emulate the human Lombard effect, in which speakers involuntarily adjust their vocal production to preserve intelligibility in the presence of noise. As a result, their communicative reliability degrades under adverse acoustic conditions. To remain intelligible in noisy environments, conversational agents require a mechanism to emulate noise-induced adaptive behavior during synthesis. We propose NIL-TTS, a noise-induced Lombard TTS framework. We integrate a feature-wise linear modulation (FiLM) mechanism into a flow-matching backbone for dynamic vocal adaptation. A two-stage training paradigm is utilized: initial supervised fine-tuning on paired Lombard corpora, followed by Flow-GRPO post-training to optimize for intelligibility. Evaluations confirm that NIL-TTS autonomously adjusts its output in response to varying noise conditions, exhibiting key correlates of the human Lombard effect. Driven by these adaptive modulations, the synthesized speech achieves significant intelligibility improvements that scale proportionally with noise intensity. Ultimately, this work presents a flow-matching and reinforcement learning-based advancement within the adaptive Lombard speech synthesis paradigm for challenging real-world scenarios.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20181
PDF
-
ACR-Net: Mitigating Semantic Dominance via Contrastive Acoustic-Semantic Decoupling
2026-10-14 12:42:55Mengke Zhang, Yanda Shao, Tianhe Wu, Kai FengAbstract
Speech Emotion Recognition (SER) models and Audio LLMs fail when vocal tone contradicts textual semantics. Extensive training on congruent data engenders a rigid semantic prior, causing "Semantic Dominance", a severe text bias during cross-modal conflicts. We introduce the ASPIRE benchmark of adversarial audio-text pairs across four conflict types, alongside two metrics: Semantic Overconfidence Penalty (SOP) and Latent Decoupling Degree (LDD). Unlike fusion methods that merely re-weight collapsed features, our Acoustic Conflict Resolution Network (ACR-Net) uses Cross-Modal Attention and Contrastive Decoupling Loss to disentangle contradictory representations into orthogonal latent spaces. Experiments show existing models fail under polarity conflicts due to extreme semantic overconfidence. Conversely, ACR-Net preserves acoustic fidelity, maintaining high LDD to drive SOP near zero, achieving superior Acoustic Accuracy (ACC) in both adversarial and standard scenarios.
Abstract (translated)
URL
https://arxiv.org/abs/2610.20114
PDF
-
Few-Shot Adaptation for Anomalous Sound Detection at Low False-Positive Rate
2026-10-14 09:51:03Simone Lugani, Christof Pichler, Hannes Wegleiter, Christoph Feilmayr, Rodolfo Zunino, Edoardo RagusaAbstract
Reliable fault detection is crucial for industrial machinery in large-scale facilities, where unexpected failures can cause costly downtime and production interruptions. Although acoustic monitoring provides a scalable solution for machine condition monitoring, its deployment is often constrained by high false-positive rates. We propose a two-step approach to automatic anomalous sound detection: first, when only normal operating audio samples are available, a regularized unsupervised model is deployed; then, after the first machine fault, a few-shot adaptation strategy exploits the anomalous samples acquired during the fault to improve the system. This adaptation increases the area under the precision-recall curve (AUPR) from 23.0 to 64.0 and improves detection in the low false-positive-rate region, with the partial area under the receiver operating characteristic curve (pAUROC) increasing from 39.6 to 72.8. We also show that covariance regularization improves both prediction accuracy and training stability in the unsupervised model. These results provide a practical strategy for acoustic anomaly detection systems, enabling improved accuracy and reduced false alarms.
Abstract (translated)
URL
https://arxiv.org/abs/2610.19893
PDF
-
Long-Prompt Zero-Shot TTS: Benchmarking Prosody Reproduction Beyond Voice Cloning
2026-10-14 08:23:44Dekun Chen, Renjie Xu, Yingda Shen, Qinke Ni, Zhizheng WuAbstract
Zero-shot text-to-speech (TTS) typically conditions on a short speech prompt, which conveys timbre but provides only limited evidence of a speaker's prosodic patterns. We formulate Long-Prompt Zero-Shot TTS, a task that uses long speech prompts at inference time to reproduce speaker-specific prosody without target-speaker fine-tuning. We introduce LP-TTS-Bench, a bilingual benchmark of 37 speakers with 63.95 prompt hours and 2,728 held-out test utterances. Beyond intelligibility and timbre similarity, LP-TTS-Bench evaluates timing, prominence, intonation, and model-based prosody metrics, enabling multidimensional evaluation of prosody reproduction. As a reference system, we develop LPTTS, whose hierarchical Long-Prompt Encoder compresses long speech prompts into a fixed number of continuous prosody vectors. Experiments show improvements in several prosodic dimensions over a short-prompt baseline, with overall gains as prompt duration increases up to approximately 10 minutes and saturation at longer durations.
Abstract (translated)
URL
https://arxiv.org/abs/2610.19774
PDF
