Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:40:16.437239Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 1 inbound Pith citation observation for arXiv:2507.02176.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:40:16.437239Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T20:40:17.636566Z
69 of 69 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation fbd3ef56-0b7a-4d69-8241-d50fb04b296f · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Virtual chara cters are expected to possess unique identities that remain consi stent across time
Reference 1
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis We explore which mark- ers are represented in some widely used ASV embeddings, and measure the effect of confounding factors
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis This reflects the lower bound of our metric, where we expect the smallest distances
Reference 4
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis This setting tests wh ether our metric can distinguish between speakers undistinguish - able with speech rate
Reference 5
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis The results in the top section of Table 3 show that rhythm distances are significantly larger between different speak ers, even those with similar speech rates
Reference 6
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis We showed that ASV embeddings mainly encode speech identity markers relating to anatomy (e.g
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis An image is worth one word: Personalizing t ext-to- image generation using textual inversion,
Reference 8
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Dreambooth: Fine tuning text-to-image d iffusion models for subject-driven generation,
Reference 9
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving
Reference 10
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Observation c89f5a9f-9e70-4ff9-a821-38f5722804a4 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models
Reference 11
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Observation 5d17b08b-2cef-42f0-b20c-065a1b807fe3 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Generat ing video game scripts with style,
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Observation 988925a3-6791-4cc9-b3ea-6d3176cae942 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Meet your favorite character: Open-domai n chat- bot mimicking fictional characters with only a few utterance s,
Reference 13
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Observation ba599d21-aa4a-44e9-9de5-a9c72b8282d5 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Information conveyed by vow- els
Reference 14
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis The perception of personal iden tity in speech: Evidence from the perception of twins’ speech,
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis X-V ectors: Robust DNN embeddings for speaker recognition,
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis ECAPA - TDNN: emphasized channel attention, propagation and aggre ga- tion in TDNN based speaker verification,
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis State-of-the-art speaker recogni tion with neural network embeddings in NIST SRE18 and speakers in the wild evaluations,
Reference 18
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Generalized end-to-end loss for speaker verifica- tion,
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Observation b5ee12e4-44fe-4f82-9642-408fa023c43c · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis We need variations in speech synthe- sis: Sub-center modelling for speaker embeddings,
Reference 20
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Predictions of subjective ratings and spoofing as- sessments of V oice Conversion Challenge 2020 submissions,
Reference 21
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Transfer learning from speaker verificat ion to multi- speaker text-to-speech synthesis,
Reference 22
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Observation d59ce44c-d9f4-412b-bedd-6b5094c18c70 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Zero-shot multi-speaker text-to-sp eech with state-of-the-art neural speaker embeddings,
Reference 23
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Observation 38425e51-2504-4a84-874e-c042e926c1d6 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Y ourTTS: towards zero-shot multi- speaker tts and zero-shot voice conversion for everyone,
Reference 24
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Observation 1cb6588a-a323-4f5c-b318-afc26a02eae4 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Investigating on incorporating pret rained and learnable speaker representations for multi-speaker mult i-style text-to-speech,
Reference 25
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Observation e3bf7555-52cb-4bf9-bd88-e292621223c0 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance
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Observation 01a602c7-7a3a-4992-bcca-2d20ea94f10d · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis The Multi-Speaker Multi-Style V oice Clo ning Chal- lenge 2021,
Reference 27
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis VoxGenesis: Unsupervised Discovery of Latent Speaker Manifold for Speech Synthesis
Reference 28
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Observation fc46cf74-cdd9-420c-b7f8-d04b70d048b7 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Evaluating text-to-speech synthesis from a large discrete token-based speech language model,
Reference 29
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Observation f6e68c49-0f0f-4721-b3a1-898330ee7271 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis A comparison of discrete and soft speech units for improved voice conversion,
Reference 30
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Observation 9fdce733-cd03-4761-b18d-ef7f05f91428 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis V oicebox: Text-guided multilingual univ ersal speech generation at scale,
Reference 31
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Neural codec language models are zero-s hot text to speech synthesizers,
Reference 32
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Observation fc5d7913-3f76-4168-90e3-16b024875a87 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis The Singing V oice Conversion Chall enge 2023,
Reference 33
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Observation f3ef1777-0b8f-4ab0-9ddc-4580ee3c651c · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Generative Data Augmentation Challe nge: Zero- shot speech synthesis for personalized speech enhancement ,
Reference 34
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Acoustic properties of voice timbre t ypes and their influence on voice classification,
Reference 35
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Speech production patterns in producing l inguis- tic contrasts are partly determined by individual differen ces in anatomy,
Reference 36
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Unresolved cited work
Reference 37
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Observation cae77f0c-21b8-4b0b-8ad9-32e71334c683 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis A study of rhythm in London: Is syllable-timing a feature of Multicultural London English ?
Reference 38
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis The measurement of rhythm: a comparison of Sin- gapore and British English,
Reference 39
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Sociophonetics of phonotactic pheno mena in french,
Reference 40
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Observation 4d90a904-75f6-4cf3-8437-f4ba2f576871 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Individual di fferences in vowel production,
Reference 41
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Individual differences in speech pro- duction: V oice-onset-time,
Reference 42
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis The social life of phonetic s and phonology,
Reference 43
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Observation a0a78c3e-a471-4b32-9051-7867fd9eefd9 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Prosodic rhythm and Afric an American english,
Reference 44
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Observation dd9e3107-7a7a-413a-82ef-74ec4916a3e9 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis La liaison sans enchaˆ ınement,
Reference 45
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Observation f7e19809-8f4a-47d6-b320-721f6b2d4a4b · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Stuart-Smith, E
Reference 46
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis The Blizzard Challenge 2023,
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Refining the evaluation of speech synthesis: A summ ary of the blizzard challenge 2023,
Reference 48
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Good practices for evaluation of synthesized speech,
Reference 49
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Stuck in the MOS pit: A critical anal ysis of MOS test methodology in TTS evaluation,
Reference 50
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation
Reference 51
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis MOS vs . AB: evaluating text-to-speech systems reliably using cluster ed stan- dard errors,
Reference 52
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis V oxSim: A perceptual voice similarity da taset,
Reference 53
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Salmon: A suite for acous tic language model evaluation,
Reference 54
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Automatic evaluation of speaker simila rity,
Reference 55
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Observation 36a86cc9-028d-4d05-b97d-d2dfadd18548 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis SVSNet: An end-to-end speaker voice simi larity assessment model,
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Observation 502ce3f8-0886-4a72-9e6a-87181406f5a4 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis The V oxCeleb Speaker Recognition Challe nge: a retrospective,
Reference 57
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Observation d98022c9-0b21-4b97-a133-f6480c3df776 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis SpeechBrain: A General-Purpose Speech Toolkit
Reference 58
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis WavLM: large-scale self-supervised pr e-training for full stack speech processing,
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Observation 65b8ea99-700f-4336-a54f-b31272b74d15 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis The CMU Arctic speech databa ses,
Reference 60
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Observation 56a9ff04-0491-43ae-bee3-ac8a523caf54 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis L2-ARCTIC: a non-native english speech c orpus,
Reference 61
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Lib- riSpeech: an ASR corpus based on public domain audio books,
Reference 62
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Observation e75cb6d1-b3e3-4a99-93e8-8046229d5370 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Analysis of fun- damental frequency, jitter, shimmer and vocal intensity in chil- dren with phonological disorders,
Reference 63
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Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis V ocal acoust ic analy- sis – jitter, shimmer and HNR parameters,
Reference 64
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Observation d80aa272-c9b8-486a-88f8-28b67beccaef · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis V ariation of the acoustic parameters: f0, jitter, shimmer and alpha ratio in relation with different background noise levels,
Reference 65
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Observation 6bc0d3b7-7283-4f48-948d-5ea32a83db7b · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Rhyth m modeling for voice conversion,
Reference 66
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Observation f993c02a-30c5-4bb9-832e-82ceaf2ffa39 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Opensmile: the munich versatile and fast open-source audio feature extractor,
Reference 67
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Observation 35d8cdf7-a228-477d-8e01-9ae7453031b1 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis ASRRL-TTS: Agile Speaker Representation Reinforcement Learning for Text-to-Speech Speaker Adaptation
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Observation fd0d3d8b-c6c7-42cb-afba-aeecf2eaf8f8 · outbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis All about audio equalization: Solu- tions and frontiers,
Reference 69
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Observation 517abf04-5934-49ef-95b2-e23ab2515c83 · inbound
Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis
Reference 2
Source-reported events for the cited work
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