Speech Prosody

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Speech prosody concerns the patterns of pitch, stress, rhythm, duration, and pausing that shape how speech is understood. Researchers analyze acoustic contours and phonological annotations, probe self-supervised speech representations, and build neural systems that predict or control prosodic features in tasks such as text-to-speech, tone recognition, and pronunciation assessment. Evaluation spans multilingual stress and tone tests, emotional speech perception, dialogue and translation, and comparisons with human judgments. A central challenge is that models may encode prosodic cues without reliably using or reproducing them, especially when fine-grained meaning depends on context, language, or speaker.

Latest papers

80 papers10 papers in the last four weeks, down 23% on the four weeks before. 0.1% of all new papers.

Aug 5, 2026

The interface of intonation and lexical tone: Boundary phenomena in Mandarin varieties

This chapter explores the intricate interplay between intonation and tone in Mandarin Chinese varieties, focusing on f0, the primary acoustic cue for both intonation and tone. The main empirical base is intonation boundary phenomena, where intonation and tone intersect and influence each other in conveying a range of sentence-level linguistic functions -- such as question vs. statement -- and a rich array of speakers' attitudinal information. Theoretical models and emerging techniques are also discussed to account for the observed interactions of tonal aspects and boundary phenomena to convey multiple levels of communicative meanings.

Cong Zhang, Yiya Chen

Jul 30, 2026

Correlation between prosody and pragmatics: A case study of the discourse marker hālā `now' in Persian

The Persian discourse marker hālā ('now') exhibits remarkable multifunctionality, extending far beyond its temporal adverbial role to encompass a variety of pragmatic functions. This study presents a pragmatic and acoustic analysis of hālā in spoken Persian, examining 267 instances from spontaneous conversations. While temporal uses were present, they were often combined with other discourse marker functions, indicating extensive multifunctionality, with 70% of tokens serving two or more pragmatic roles. Textual functions (topic shifting, signaling relationships, boundary marking, attention guidance, topic introduction, and topic emphasis) were most frequent, followed by interactive functions (turn management, listener engagement, and feedback regulation), and modal functions (epistemic stance, emotional expression, and attitudinal marking). Prosodic analysis revealed that duration and intensity are key cues for distinguishing hālā's functions. Textual uses were significantly shorter, while temporal uses showed a tendency toward longer realizations. Interactive functions correlated with higher intensity, while modal functions showed a weaker tendency toward lower intensity. These findings indicate that duration and intensity are the main prosodic cues associated with functional differentiation in hālā, especially in textual and interactive uses.

Soleiman Ghaderi, Moloud Asakereh, Kevin Tang

Department of Linguistics, Faculty of Foreign Languages, University of Isfahan, Isfahan, Iran · Department of English Language and Linguistics, Institute of English and American Studies, Faculty of Arts and Humanities, Heinrich Heine University Düsseldorf, Germany

Jul 22, 2026

StellarTTS: Sparse Temporal Embedding for Low-Latency and Robust Speech Synthesis

The trade-off between robustness, latency, and prosody critically challenges text-to-speech (TTS) systems. Autoregressive models, despite fidelity, are slow and error-prone; non-autoregressive (NAR) alternatives, while fast, often sacrifice prosodic naturalness via rigid alignments. This paper introduces StellarTTS, a novel mobile-optimized NAR TTS framework based on a sparse temporal embedding strategy, enabling granular control of phoneme duration, pronunciation, and prosody. Furthermore, we propose a semantic-aware codec that facilitates efficient single-stage decoding. Conditioned on the sparse temporal embedding, our 83M-parameter lightweight masked generative transformer achieves a real-time factor (RTF) of 0.08. Experiments demonstrate that StellarTTS attains lower latency and stronger robustness compared to state-of-the-art TTS systems, while maintaining competitive performance in audio quality, prosodic naturalness, and speaker similarity.

Kaicheng Luo, Xuefei Gong, Yutao Sun +6

Honor Device Co., Ltd., China · Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence, AI Institute School of Computer Science, Shanghai Jiao Tong University, Shanghai, China

TTS Synthesis Speech Prosody

Jul 17, 2026

A Situational Speech Synthesizer for Yoruba: System Design, Phonological Rule Architecture, and Orthographic Extensions for Contour

We present TTSYoruba, a rule-based concatenative diphone speech synthesizer for Yoruba, deployed at online as part of the YorubaName.com open dictionary of Yoruba personal names. The system takes tone-marked Yoruba text as input and produces audio output by applying a hand-crafted phonological rule system to a recorded inventory of 651 diphone units spanning five tonal variants of every consonant-vowel combination in the language. We describe the phonological architecture of the system in detail, including our complete tonal file-selection logic, our treatment of the three-way nasal disambiguation problem (oral /n/, nasalized vowel, and syllabic nasal), and the derivation of contextual rising and falling tones from level-tone input. We also present, as an orthographic contribution, the adoption of the caron and circumflex, which are symbols with prior standing in Yoruba phonological transcription, as standard single-vowel contour tone markers, integrated into the TTS normalization pipeline and the WriteYoruba keyboard input tool. The system's performance was evaluated through a listener study (N=50), with detailed results on Mean Opinion Scores (MOS) presented in Section 6. Keywords: Yoruba, text-to-speech, low-resource languages, diphone synthesis, contour tones, African language NLP, rule-based synthesis

Kola Tubosun, Adedayo Oluokun, Hafiz Adewuyi +1

TTS Synthesis Low-Resource TTS Synthesis Speech Prosody

Jul 15, 2026

Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring

L2 speech assessment has traditionally focused on phonetic assessment, leaving the scoring of suprasegmental features such as rhythm and intonation underexplored. Moreover, assessment methods often require training with labeled L2 speech data, making them difficult to apply in low-resource settings. We investigate whether DTW over self-supervised WavLM representations can provide a text-free framework for assessing phonetic accuracy, rhythm, and intonation in English and Japanese L2 speech. Results show that a basic DTW-based approach that compares learner speech to native templates exceeds human agreement on holistic and sentence-level phonetic scoring. For rhythm, we introduce methods that measure the degree of warping in the DTW alignment path; our best method approaches human-level performance. For intonation, we combine DTW distance over prosodic residuals with pitch and intensity features, but performance remains more modest on some tasks. Our results point to self-supervised representations as a promising, text-free basis for multi-aspect pronunciation assessment.

Stephen McIntosh, Reuben Smit, Daisuke Saito +2

The University of Tokyo, Japan · Stellenbosch University, South Africa

Dynamic Time Warping Speech Processing Speech Prosody

Jul 8, 2026

SAMPA: Transformer-based segmentation of prosodic boundaries in Brazilian Portuguese

Automatic prosodic segmentation identifies boundaries between speech units based on acoustic and linguistic evidence. Although recent deep learning approaches have achieved strong results for English, automatic segmentation for Brazilian Portuguese (BP) still relies largely on rule-based or traditional machine-learning methods. This paper presents SAMPA, a Whisper-based segmenter that transcribes BP speech while inserting explicit markers for terminal prosodic boundaries. We fine-tune Whisper large-v3 on manually segmented recordings from the NURC-Sampa dataset and evaluate different training and test-time filtering configurations, including out-of-distribution testing on the MuPe-Diversidades dataset. Without test-time filtering, the best models reach f1 binary of 0.730 on the held-out test split and 0.793 on MuPe-Diversidades, outperforming an out-of-the-box Whisper baseline and a retrained feature-based classifier. Finally, n-gram and acoustic-visual analyses provide qualitative observations that help interpret the model's predictions and formulate hypotheses about the linguistic and prosodic cues that may influence its boundary decisions.

Rodrigo de Freitas Lima, Julio Cesar Galdino, Marcos Vinicius Treviso

ICMC University of São Paulo São Paulo, Brazil · Instituto Superior Técnico University of Lisbon Lisbon, Portugal

Speech Prosody Speech Language Models Boundary-Aware Segmentation

Jul 7, 2026

WordVoice: Explicit and Decoupled Multi-Dimensional Word-Level Control for LLM-Based TTS

While recent Large Language Model (LLM)-based Text-to-Speech (TTS) systems have achieved remarkable naturalness, they predominantly rely on implicit end-to-end generation paradigms, resulting in coarse-grained control. In scenarios demanding precise stylistic interventions and strict temporal alignment, such as audiobook narration and video dubbing, the inability to explicitly manipulate word-level acoustic attributes remains a critical bottleneck. This limitation is primarily amplified by the severe scarcity of fine-grained annotated datasets and the architectural challenge of integrating multi-dimensional control signals into discrete autoregressive generation. To address this, we propose a unified framework for highly precise word-level control. First, we construct WordVoice-5A, a massive 4.7k-hour bilingual dataset featuring five-dimensional word-level annotations (duration, boundary, energy, pitch and tone) developed through a rigorous linguistically-guided pipeline. Second, we introduce WordVoice to transform the implicit generation process into an explicit, highly controllable paradigm. Specifically, we introduce a bound-token mechanism within the LLM to formulate an explicit ``acoustic planning'' process, enabling adaptive multi-task prosodic planning and flexible manual intervention. Furthermore, we augment the token-to-waveform stage with a fine-grained acoustic modulation module, bridging the resolution gap to strictly align word-level attributes between highly compressed discrete tokens and continuous waveforms. Extensive experiments demonstrate that WordVoice achieves superior, decoupled control over multiple acoustic dimensions while maintaining competitive zero-shot synthesis stability. The code and audio samples are publicly available at https://xxh333.github.io/wordvoice-demo/.

Sihang Nie, Jinxin Ji, Xiaofen Xing +4

South China University of Technology · Huya Inc. · Tongji University +2

TTS Synthesis Speech Prosody Controllable Speech Generation Speech Language Models

Jul 2, 2026

Using embeddings to predict spoken word duration and pitch in Mandarin monosyllabic words

Time-normalized f0 contours of Mandarin words in conversational speech have been shown to be predictable in part from their contextualized embeddings (CEs). The present study investigates whether CEs also predict spoken word duration for 7470 tokens of Mandarin monosyllabic CV words extracted from a Mandarin corpus of spontaneous speech. We show that CEs indeed are predictive for duration, above chance level, not only at the type level, but also at the level of individual tokens, as indicated by the results obtained with the type-wise and token-wise permutation baselines. We also show that the predicted durations are sufficiently precise to back-transform predicted f0 contours in [0,1] normalized time to contours on the ms time scale. The resulting predicted contours approximate empirical contours and also outperform a permutation baseline.

Xiaoyun Jin, Mirjam Ernestus, R. Harald Baayen

Quantitative Linguistik, University of Tuebingen, Germany · Center for Language Studies, Radboud University, 6525 HT Nijmegen, The Netherlands

Speech Processing Speech Prosody

Jun 30, 2026

Reference-Based Prosody and Rhythm Evaluation for Spoken Dialogue Systems

Speech-to-speech (S2S) AI agents are advancing rapidly, yet evaluation lacks interpretable speech-native measures for conversational prosody and rhythm. Because

F_0

, speaking rate, articulation rate, and pausing shift with model-predicted speaker traits and interaction state, pooled human statistics can be poorly calibrated for evaluating a particular output. Using 4000+ hours of dyadic English conversation from the Seamless Interaction dataset, we construct matched reference regimes for

F_0

mean,

F_0

expressivity, speech rate, articulation rate, pause ratio, and mean pause duration. We then define a percentile-based evaluation protocol: extract the same metrics from an S2S output waveform, compare them to the closest matched human reference stratum, and report percentile deviations or 5th-95th percentile out-of-regime flags. On held-out human rows, pooled references over-flag state-conditioned

F_0

expressivity and rhythm, while matched references return flag rates closer to the nominal 10% and make deviation direction interpretable. These outputs serve as behavioral plausibility checks that complement, rather than replace, perceptual and user-centered evaluation.

Ashish Hallur, Thomas Thebaud, Georgi Tinchev +2

Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA · Amazon Inc., Seattle, WA, USA

Voice Agent Evaluation Speech Generation Evaluation Spoken Dialogue Systems Speech Prosody

Jun 26, 2026

HPRO: Hierarchical Progressive Reward Optimization via Preference Extraction for Emotional Text-to-Speech

Recently, Large Language Model (LLM)-based Text-to-Speech (TTS) models have achieved remarkable naturalness. However, the standard Supervised Fine-Tuning paradigm often converges to statistically averaged prosody, limiting emotional expressiveness. While preference-driven optimization offers a promising alternative, existing approaches suffer from two structural mismatches: information conflict, where content and emotion in a shared latent space produce conflicting gradients, leading to reward hacking and semantic degradation; and scale gap, where sparse sentence-level rewards struggle to guide dense frame-level generation. To overcome these challenges, we propose HPRO, a hierarchical progressive reward optimization framework. Within HPRO, we introduce the HD-Emo codec as a novel differentiable reward model to mitigate the information conflict. It extracts speech into distinct content and style preference tokens, structurally isolating emotional optimization from semantic content. Building upon this structured preference space, HPRO bridges the scale gap by progressively aligning frame-, word- and sentence-level objectives. Experiments demonstrate that HPRO significantly enhances emotional expressiveness, while effectively preserving linguistic intelligibility. The code and audio samples are publicly available at https://xxh333.github.io/hpro-demo/.

Sihang Nie, Xiaofen Xing, Rui Xing +5

South China University of Technology, China · Huya Inc., China · Tongyi Fun Team, Alibaba Group, China +1

Reward Modeling Speech Prosody Text-to-Speech Emotional Speech Synthesis

Jun 26, 2026

Do Speech Emphasis Models Generalize across Languages and Emotions?

Prosodic emphasis varies across languages, emotions, and speaking styles, yet existing emphasis detection models are largely trained and evaluated on monolingual neutral read speech. We introduce MMEE (Multilingual Multi-Emotion Emphasis), a corpus of 10,000 professionally recorded expressive utterances (14.13 hours) across 7 languages and 34 emotion/style categories, with three-level perceptual labels (10 annotations per sample). We benchmark two state-of-the-art architectures under monolingual, cross-lingual, multilingual, cross-emotion, cross-dataset, and data-scale settings. Monolingual models show limited zero-shot transfer, degrading across typologically distant languages, while multilingual training substantially improves robustness. Models transfer robustly between high- and low-arousal emotions; bidirectional transfer between synthetic and perceptual benchmarks suggests shared prosodic structure; and performance stays robust even at smaller training scales.

Megan Wei, Deepali Aneja, Jiaqi Su +3

Adobe Research, USA · Brown University, USA

Speech Prosody Cross-Lingual Transfer

Jun 24, 2026

Phonetic and semantic analyses of spoken corpora of Beijing and Taiwan Mandarin indicate that the neutral tone is a lexical tone

The neutral, or floating, tone of Mandarin Chinese is a tone with an enigmatic set of properties. It has been described as a reduced tone, or as a tone that sometimes is lexically fixed but that can also be toneless. In two-syllable words, it is found only on the second syllable, but single-syllable words can also have the neutral tone. We present a corpus-based study of the phonetic realization of the neutral tone in spontaneous conversational speech corpora of Beijing Mandarin and Taiwan Mandarin. We show that the neutral tone has its own tonal target, just as the four lexical tones of Mandarin. We also show that disyllabic words with a neutral tone have pitch contours that have a pitch component that depends on the tone on the first syllable, just as has been observed for two-syllable words with a lexical tone on the second syllable (Chuang et al., 2026). Furthermore, words with a floating tone have word-specific pitch signatures, which have also been documented for single-syllable words (Jin et al., 2026) as well as two-syllable words (Lu et al., 2026b). These word-specific pitch signatures are shown to be predictable to some extent from words' contextualized embeddings, as previously reported for lexical tones (Chuang et al., 2026; Lu et al., 2026b). As there is also considerable variability in the realization of lexical tones, we propose that the neutral tone is, in fact, a lexical tone in both Taiwan Mandarin and Beijing Mandarin. We document both similarities and differences in the realization of the floating tone in these two varieties and provide evidence, using contextualized embeddings, that some of the observed differences may arise from differences in the meanings of the words as used in the two corpora.

Yuxin Lu, Zhexuan Li, R. Harald Baayen

Quantitative Linguistics, Eberhard Karls Universität Tübingen, Tübingen, Germany

Jun 24, 2026

Adaptive Oscillatory Inductive Bias for Modeling Sharp Prosodic Dynamics in Diffusion-Based TTS

Diffusion-based text-to-speech (TTS) models have achieved significant improvements in speech quality. However, modeling sharp prosodic transitions and rapid pitch variations in expressive speech remains challenging. Existing diffusion-based TTS decoders commonly utilize periodic nonlinearities such as Snake activation function to capture harmonic structures, but this activation funcation provides limited adaptability when modeling abrupt amplitude and frequency variations. In this paper, we investigate the role of oscillatory inductive bias in diffusion-based TTS decoders and introduce an adaptive oscillatory nonlinearity that enables controllable periodic modulation while maintaining signal stability through a linear bypass component. We refer the resulting TTS system as OscillaTTS. Experiments on the LJSpeech and Emotional Speech Dataset show consistent improvements across objective and subjective evaluations, indicating improved modeling of expressive prosodic dynamics.

Sandipan Dhar, Nirmesh J. Shah, Ashishkumar P. Gudmalwar +1

Media Analysis, Sony Research India

TTS Synthesis Audio Diffusion Models Expressive Speech Synthesis Speech Prosody

Jun 22, 2026

Synthesizing the Lombard Effect: Multi-Level Control of Speech Clarity and Vocal Effort in TTS

Humans tend to speak louder and clearer in challenging environments, such as noisy conditions or when addressing hearingimpaired listeners, which is called Lombard effect. To simulate this behavior in speech synthesis systems, we introduce a flow-matching based text-to-speech (TTS) model trained with vocal effort and articulation pseudo-labels. The proposed model achieves continuous and disentangled control of vocal effort and articulation, while also enabling word-level emphasis for clarifying specific segments of an utterance. Experimental results show that these control mechanisms effectively improve clarityrelated acoustic features. Furthermore, speech-in-noise experiments demonstrate that our model successfully simulates the intelligibility gains of human clear speech in noisy conditions.

Seymanur Akti, Alexander Waibel

Karlsruhe Institute of Technology (KIT), Germany · KIT Campus Transfer (KCT), Germany · Carnegie Mellon University (CMU), USA

TTS Synthesis Speech Prosody Controllable Speech Generation

Jun 20, 2026

ProsoCodec: Prosody-Oriented Speech Codec for Voice Conversion

Neural speech codecs efficiently compress speech and have become a foundation for speech generation, but they are typically learned as holistic representations that intertwine linguistic content, speaker identity, and prosody. While this design is effective for zero-shot voice cloning, it hinders downstream tasks that require prosody preservation or transfer, such as voice conversion. To address this, we introduce ProsoCodec, a prosody-oriented speech codec that models prosody as a conditional residual rather than as a disentangled stream. Specifically, by conditioning both the encoder and decoder on text and speaker embeddings as prefix tokens, the discrete bottleneck is encouraged to capture prosodic variation not explained by content and speaker. To further preserve prosody, we use the low-frequency mel band and train the model on paired same-speaker utterances. Experiments on voice conversion show improved prosody preservation and reduced source-timbre leakage.

Jeongsoo Choi, Ji-Hoon Kim, Shujie Hu +1

KAIST, South Korea · Chung-Ang University, South Korea · The Chinese University of Hong Kong, China

Audio Representation Learning Neural Audio Codecs Speech Prosody Voice Conversion

Jun 19, 2026

LLM-Based Multi-Reference Evaluation for Efficient and Robust Assessment of Phrase Break Annotations

Reliable evaluation of phrase break annotations is crucial, as subtle variations in prosodic boundaries directly affect the clarity and naturalness of speech. However, existing approaches exhibit major limitations: single-reference evaluation assumes a unique gold phrasing for an utterance despite multiple valid phrasings, while human judgment, though flexible, is labor-intensive and unscalable. To address these, we propose LLM-based Multi-Reference Evaluation (LMRE) for phrase break annotations that models the one-to-many nature of prosodic phrasing and generates multiple valid phrasings from minimal demonstrations. On a Korean testbed of 1,356 annotations covering five strategies, LMRE shows stronger alignment with human judgment than single-reference evaluation in both acceptance behavior and score correlation. Our findings demonstrate that LMRE effectively achieves both scalability and multi-reference support, highlighting the potential of LLMs for evaluation in the speech domain.

Younghan Park, Hoyeon Lee, Hawon Jeong +1

NAVER Cloud, South Korea · Yonsei University, South Korea · KAIST AI, South Korea

Speech Prosody Automated Evaluation

Jun 18, 2026

PASQA: Pitch-Accent-Focused Speech Quality Assessment Model Trained on Synthetic Speech with Accent Errors

Existing mean opinion score (MOS) prediction models typically predict utterance-level naturalness MOS and can be insensitive to localized pitch-accent errors. We propose Pitch-Accent-focused Speech Quality Assessment (PASQA), which explicitly targets pitch-accent correctness. To train our model, we construct a controlled Japanese accent-error dataset by changing accent patterns using an accent-controllable text-to-speech system, and compute a pseudo accent-quality score from the accent-error rate. PASQA builds on self-supervised representations and employs mora-conditioned fusion, ranking loss, an auxiliary accent-error localization task, and speaker-invariant training. Experiments show that conventional models fail to preserve the ordering by accent-error severity, whereas PASQA achieves high ordering accuracy on both seen and unseen speakers. Further, PASQA shows stronger agreement with human accent-correctness judgments. The code is available at https://github.com/lycorp-jp/PASQA.

Masaya Kawamura, Yuma Shirahata, Kentaro Mitsui +1

LY Corporation, Japan

Speech Quality Assessment Speech Prosody

Jun 16, 2026

MagpieTTS-LF: Inference-Time Long-Form Speech Generation Without Training on Long-Form data

Neural Text-to-Speech (TTS) systems achieve remarkable quality on short utterances but long-form speech generation shows prosodic drift, speaker inconsistencies and sentence boundary artifacts. Existing approaches either compress sequences, increase context length or naively concatenate independently synthesized chunks. We present an inference-time approach called MagpieTTS-LF that enables MagpieTTS to produce coherent long-form speech without model retraining. Our method introduces three key innovations: (1) soft attention priors to guide monotonic alignment while preserving past and future context; (2) a stateful inference algorithm that maintains context across sentence chunks, ensuring prosodic continuity; (3) history-aware text encoding that uses past text for discourse-level prosodic planning. Experiments on long texts show significant improvements in long-range intelligibility, prosodic coherence, speaker consistency, and boundary naturalness compared to other baselines.

Subhankar Ghosh, Jason Li, Paarth Neekhara +4

NVIDIA Corporation, USA

TTS Synthesis Speech Prosody Long-Form Speech Recognition Long-Form TTS Synthesis

Jun 16, 2026

Perceptual compensation for tonal context in self-supervised speech models

This study examines the extent to which the wav2vec2.0 architecture exhibits evidence of compensation for phonological context. We conducted a pseudo-replication of a perceptional compensation experiment on Mandarin Chinese tones, and compared the embedding similarities and probing classifier outputs between a purely self-supervised pre-trained model and a model fine-tuned for Mandarin ASR. No evidence of compensation was found in the embedding similarities of the purely pre-trained model. Probing classifiers showed some evidence of compensation in addition to the expected layer-wise improvements in categorization, but failed to replicate human performance on isolated test syllables. Our findings contrast with previous reports of sensitivity to phonological structure emerging through pre-training alone, and suggest that supervised objectives may be necessary to encourage the abstraction of at least some types of phonological regularities.

James Kirby, Ioana Krehan, Michele Gubian

Institute for Phonetics and Speech Processing, LMU Munich, Germany

Supervised Fine-Tuning Speech Prosody Self-Supervised Speech Representation Learning

Jun 13, 2026

Dynamic Prosody Prediction in LLM-based TTS for Improving Speaker Similarity

Personalized text-to-speech (TTS) aims to clone the target speaker in the synthesized speech, imitating both the voice and speaking style. Current large language model (LLM)-based TTS methods ignore the style-specific prosodic patterns in generated speech, resulting in deficient style learning and thus limiting speaker similarity in synthesized speech. To this end, we investigate the prosody learning conditioned on the synthesized speech, and propose to predict the prosody of the current syllable based on previously predicted speech. Experimental results obtained on three datasets demonstrated the efficacy of the proposed dynamic prosody prediction method in enhancing the prosody learning capability, thereby improving the speaker similarity of the generated speech. Audio samples are available at https://muzw.github.io/dynapros/.

Zhenwei Mou, Liping Chen, Yajun Hu +3

University of Science and Technology of China, Anhui, China · iFLYTEK, Anhui, China

TTS Synthesis Speech Prosody Voice Cloning

Jun 11, 2026

NaturalFlow: Reducing Disruptive Pauses for Natural Speech Flow in Simultaneous Speech-to-Speech Translation

Simultaneous speech-to-speech translation aims to enable near-real-time communication by minimizing latency, offering a compelling, real-time alternative to the high latency of consecutive translation. However, the excessive pursuit of low latency often results in fragmented chunk-wise speech. Consequently, listeners are subjected to an unnatural acoustic flow punctuated by frequent pauses, which could increase their cognitive load. To bridge this gap, we introduce a fluency-aware optimization framework designed to discover the sweet spot between the low-latency benefits of simultaneous translation and the natural flow of consecutive translation. Our framework minimizes inter-chunk silences by leveraging model-internal signals, including linguistic diversity and induced temporal variability in speech durations. Experiments on short- and long-form benchmarks show that our framework produces natural speech flow while maintaining competitive latency and translation quality.

Dongwook Lee, Youngho Cho, Sangkwon Park +2

IPAI and 2ECE, Seoul National University, Seoul, 08826, Korea · 2ECE, Seoul National University, Seoul, 08826, Korea · Department of AI, University of Seoul, Seoul, 02504, Korea

Speech Translation Simultaneous Speech Translation Speech Prosody

Jun 11, 2026

PRISM: Prosody-Integrated Multi-Agent Reasoning Framework for Empathetic Spoken Dialogue

Empathetic spoken dialogue systems require not only semantically appropriate responses but also emotionally aligned prosodic expression. However, cascade pipelines often discard acoustic cues during speech-to-text conversion, while end-to-end speech models lack interpretable control over emotion and knowledge integration. To address these challenges, we propose PRISM, a multi-agent framework for empathetic spoken dialogue that decouples speech perception, response generation, and speech synthesis into coordinated components. PRISM introduces a prosody-to-language translation mechanism to stabilize large language model reasoning and enables on-demand invocation of external knowledge tools for empathetic dialogue generation. Experimental results demonstrate that PRISM achieves consistent improvements in empathy, prosodic appropriateness, and text response generation quality across objective and subjective metrics. Our code is available at: https://github.com/Bxzfrm/PRISM.

Wen Zhang, Xiaocui Yang, Zhuoyue Gao +3

School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China

Multi-Agent LLM Systems Spoken Dialogue Systems Speech Prosody Emotion-Aware Dialogue Generation

Jun 8, 2026

What Makes Synthetic Speech Sound Sarcastic? A Prosody-Controlled Perception Study

Prosody plays an important role in sarcasm perception, yet previous studies have relied on naturally produced speech that lacks fine-grained control over individual acoustic dimensions. As prosodic cues co-vary in natural data, isolating their independent contributions remains challenging. We introduce a controlled framework using neural text-to-speech (TTS) with prompt-based prosodic conditioning to manipulate speech rate, pitch variation, and loudness. An orthogonal stimulus set was constructed to enable causal testing of prosodic cue effects. Human listeners rated sarcasm and naturalness, and their judgments were compared with predictions from a foundation model capable of processing audio input. Results show that loudness primarily drives human sarcasm perception, whereas the model assigns greater weight to speech rate, indicating limited behavioral alignment. This study shows how controllable neural TTS enables investigation of prosodic cue weighting in speech perception.

Zhu Li, Shekhar Nayak, Matt Coler

Speech Technology Lab, University of Groningen, The Netherlands

Jun 4, 2026

ProSarc: Prosody-Aware Sarcasm Recognition Framework via Temporal Prosodic Incongruity

We present ProSarc, an audio-only framework that detects sarcasm by modelling temporal prosodic incongruity, that is, the mismatch between local prosodic dynamics and the utterance-level emotional baseline. Dual encoding paths, a Global Emotion Encoder and a Temporal Prosody Encoder (BiLSTM + multi-head attention), feed a Prosodic Incongruity Analyzer that produces a scalar incongruity score for classification. Monte Carlo dropout provides uncertainty estimates, and an attention-based mechanism localises sarcastic onset without frame-level labels. ProSarc outperforms prior audio-only methods on MUStARD++ (F1=75.3) and generalises to spontaneous (PodSarc, F1=62.9) and cross-lingual speech (MuSaG, F1=65.6). Ten-run validation confirms the contribution of incongruity modelling (Wilcoxon p=0.002, Cohen's d=1.51). Human evaluation shows that model uncertainty tracks perceptual ambiguity and predicted onsets align with human-annotated temporal windows.

Prathamjyot Singh, Ashima Sood, Sahil Sharma +1

Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India · School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom · School of Computing, Ulster University, Belfast, United Kingdom

Audio Understanding Speech Prosody Sarcasm Detection

Jun 3, 2026

Task-Vector Arithmetic for Emotional Expressivity Control in Language-Model-Based Text-to-Speech

We investigate whether task-vector arithmetic, successful for cross-speaker emotional intensity control in modular text-to-speech (TTS), transfers to large-scale TTS systems built on language-model backbones with in-context learning (LM-TTS). Through a systematic elimination study over four progressively narrower operands on Qwen3-TTS-12Hz-1.7B - model weights via LoRA fine-tuning, continuous codec embeddings, discrete codec tokens, and the speaker embedding (x-vector) produced by an ECAPA-TDNN encoder jointly trained with the synthesis backbone - we localize the dominant carrier of emotional prosody to the x-vector. Building on this finding, we propose a training-free method based on centroid arithmetic in x-vector space: an emotion direction

τ= \mathbb{E}_i[x(s_i,\text{emo})] -\mathbb{E}_i[x(s_i,\text{neutral})]

applied to an unseen target speaker as

x_{\text{new}} = x(\text{target},\text{neutral}) + α\cdotτ

. Using ESD (English) as the

τ

source and emoUERJ (Brazilian Portuguese) as a cross-lingual ground-truth target, we observe average gains of

+0.29

in emotion2vec cosine over the ICL baseline on English held-out speakers and

+0.09

on Brazilian Portuguese held-out speakers, while largely preserving identity (WavLM SECS

\gtrsim 0.88

for the multi-speaker

τ

variant) and intelligibility (WER

\approx 0

in PT-BR). These results offer initial evidence that the dominant carrier of emotional prosody in this class of models is localizable, by elimination, to the co-trained speaker embedding, where training-free centroid arithmetic remains effective even under cross-lingual transfer.

Daniel Oliveira de Brito, Arnaldo Candido Junior

Instituto de Biociências, Letras e Ciências Exatas Universidade Estadual Paulista “Júlio de Mesquita Filho” (UNESP) São José do Rio Preto, SP, Brazil

TTS Synthesis Speaker Embedding Task Arithmetic Speech Prosody Controllable Speech Generation Emotional Speech Synthesis

May 27, 2026

Why We Need Speech to Evaluate Speech Translation

Speech translation models are increasingly capable of preserving speech-specific information (e.g., speaker gender, prosody, and emphasis), yet evaluation metrics remain blind to such phenomena. We meta-evaluate both text- and speech-based quality estimation metrics on two contrastive datasets targeting gender agreement and prosody, and find that both fall short, even when given direct access to the speech signal. We then train SpeechCOMET, a family of quality estimation models with speech encoders, and evaluate a state-of-the-art SpeechLLM as a judge. Both match or exceed text-based COMET on standard quality estimation, but neither consistently assesses speech-specific phenomena. We identify three causes: (1) speech-specific features are not reliably preserved in current encoders, (2) models tend to ignore the speech source signal, and (3) quality estimation training data contains too few relevant examples. We release all models and code, and argue that progress requires dedicated speech-specific training data and models that genuinely condition on speech.

Maike Züfle, Danni Liu, Vilém Zouhar +1

Karlsruhe Institute of Technology · ETH Zurich

Speech Translation Speech Prosody Speech Translation Evaluation MT Evaluation

May 18, 2026

Bridging the Gap: Converting Read Text to Conversational Dialogue

In recent advancements within speech processing, converting read speech to conversational speech has gained significant attention. The primary challenge in this domain is maintaining naturalness and intelligibility while minimizing computational overhead for real-time applications. Traditional read speech often lacks the nuanced prosodic variation essential for natural conversational interactions, posing challenges for applications in virtual assistants, customer service, and language learning tools. This paper introduces a novel approach, Prosodic Adjustment with Conversational Context (PACC), aimed at converting read speech into natural conversational speech used in various modern applications. PACC utilizes advanced deep neural networks to analyze and modify prosodic features such as intonation, stress, and rhythm. Unlike conventional methods, our approach uses High-Fidelity Generative Adversarial Networks (HiFi-GAN) for speech synthesis. Our experimental results demonstrate significant improvements in speech conversion, enhancing naturalness and achieving better model accuracy with additional training on speech datasets. This research establishes new benchmarks in speech conversion tasks and Mean Opinion Score (MOS) evaluation for testing model accuracy, and we show that our approach can be successfully extended to other speech conversion applications.

Parshav Singla, Agnik Banerjee, Aaditya Arora +5

Thapar Institute of Engineering and Technology, Patiala, India · Samsung Research and Development Institute, Bangalore, India

TTS Synthesis Speech Prosody Controllable Speech Generation

May 11, 2026

A Survey of Automated Presentation Coaching: Systems, Methods, and Open Challenges

Automated coaching for oral presentations sits at the intersection of computer-assisted pronunciation training (CAPT), prosody modeling, and speech synthesis, yet no prior work has systematically surveyed and compared existing systems along these dimensions. This survey reviews and categorizes automated presentation coaching systems, spanning pronunciation tutors, fluency and prosody coaches, multimodal trainers, and conference Q&A practice tools. We introduce a five-dimensional task taxonomy - covering segmental pronunciation, lexical stress, suprasegmental prosody, pacing, and content faithfulness - and explicitly map surveyed systems onto it to reveal coverage gaps. We further review the core technical methods these systems employ: TTS-based exemplar generation and diagnostic methods for pronunciation, prosody, and fluency assessment. Key open challenges include the scarcity of annotated presentation corpora, achieving accent-fair feedback across diverse L1 backgrounds, and delivering low-latency diagnostics for real-time rehearsal.

Wen Liang, Li Siyan, Zackary Rackauckas +1

Columbia University, USA · RoleGaku, USA

May 7, 2026

Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM

Speech large language models (SLMs) are typically built from text large language model (TLM) checkpoints, yet they still suffer from a substantial modality gap. Prior work has mainly attempted to reduce this gap from the output side by making speech generation more text-like, but the gap remains. We argue that the key remaining bottleneck lies on the input side. We propose TextPro-SLM, an SLM that makes spoken input more closely resemble that of a prosody-aware text LLM. TextPro-SLM combines WhisperPro, a unified speech encoder that produces synchronized text tokens and prosody embeddings, with an LLM backbone trained to preserve the semantic capabilities of the original TLM while learning paralinguistic understanding. Experiments show that TextPro-SLM achieves the lowest modality gap among leading SLMs at both 3B and 7B scales, while also delivering strong overall performance on paralinguistic understanding tasks. These gains are achieved with only roughly 1,000 hours of LLM training audio, suggesting that reducing the modality gap from the input side is both effective and data-efficient.

Wenqian Cui, Xiao-Hui Li, Daxin Tan +2

The Chinese University of Hong Kong · Huawei Technologies

Audio Understanding Speech Prosody Speech Language Models

Apr 29, 2026

DiffAnon: Diffusion-based Prosody Control for Voice Anonymization

To preserve or not to preserve prosody is a central question in voice anonymization. Prosody conveys meaning and affect, yet is tightly coupled with speaker identity. Existing methods either discard prosody for privacy or lack a principled mechanism to control the utility-privacy trade-off, operating at fixed design points. We propose DiffAnon, a diffusion-based anonymization method with classifier-free guidance (CFG) that provides explicit, continuous inference-time control over prosody preservation. DiffAnon refines acoustic detail over semantic embeddings of an RVQ codec, enabling smooth interpolation between anonymization strength and prosodic fidelity within a single model. To the best of our knowledge, it is the first voice anonymization framework to provide structured, interpolatable inference-time prosody control. Experiments demonstrate structured trade-off behavior, achieving strong utility while maintaining competitive privacy across controllable operating points.

Ismail Rasim Ulgen, Zexin Cai, Nicholas Andrews +2

Center for Language and Speech Processing, Johns Hopkins University, USA · Human Language Technology Center of Excellence (COE), Johns Hopkins University, USA

Audio Diffusion Models Speech Prosody Speaker Anonymization Privacy-Preserving Speech Processing