arXiv AI
Jun 8
dots.tts Technical Report
arXiv:2606. 07080v1 Announce Type: cross Abstract: We present dots.
Interpreting and Steering a Text-to-Speech Language Model with Sparse Autoencoders
arXiv:2606. 10029v1 Announce Type: cross Abstract: Language models increasingly serve as the backbone of text-to-speech (TTS) systems, yet we understand little about the representations they build when text and generated speech tokens share a single residual stream.
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arXiv AI
Jun 9
GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model
arXiv:2512. 20978v2 Announce Type: replace-cross Abstract: Language Model (LM)-based generative modeling has emerged as a promising direction for TSE, offering potential for improved generalization and high-fidelity speech.
Hugging Face Trending Papers
6d ago
SAEVerbalizer: Generating Explanations for Sparse Autoencoder Features via Representation Verbalization
Sparse autoencoders (SAEs) are proposed to extract numerous features from large language model (LLM) representations, yet explaining these features still relies primarily on external observation. This reliance leads to superficial explanations inferred from observed model behavior and computational inefficiency from collecting such behavioral evidence at scale.
arXiv Machine Learning
Jun 16
Scaling Human and G2P Supervision for Robust Phonetic Transcription
arXiv:2606. 16019v1 Announce Type: cross Abstract: Expert phonetic annotation is costly, especially for non-standard dialects and atypical speech.
arXiv AI
Aug 11
CuteTTS: Efficient and High-Quality Speech Synthesis via Autoregressive Modeling of Continuous Latents
arXiv:2608. 08638v1 Announce Type: cross Abstract: Zero-shot text-to-speech (TTS) now supports interactive assistants, personalized media, and accessibility tools.
arXiv AI
Jul 14
Efficiently Adapting Spoken Language Models for the Singaporean Context
arXiv:2607. 10092v1 Announce Type: cross Abstract: Spoken language models (SLMs) unify speech perception and reasoning, but adapting them to sensitive domains is underexplored, especially when the original training data is inaccessible and the use case demands multilingual, spoken-query interaction.
arXiv AI
Jun 24
ZONOS2 Technical Report
arXiv:2606. 24320v1 Announce Type: cross Abstract: We present ZONOS2 8B, our latest TTS model, which achieves state-of-the-art naturalness, prosody, and voice cloning fidelity.
Hugging Face Trending Papers
Jul 9
Cross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoders
We present a Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoder (SAE) for extracting cross-seed universal features from independently trained BERT models. Cross-seed feature universality is a fundamental challenge in mechanistic interpretability: because dictionary learning is non-convex, independently trained networks learn misaligned feature spaces, so apparently identical features may differ by random initialization.
arXiv AI
Jun 10
Speech Meets ELF: Audio Conditional Continuous-Target Diffusion for Speech Recognition and Translation
arXiv:2606. 10368v1 Announce Type: cross Abstract: Speech-to-text (S2T) systems for recognition (ASR) and translation (S2TT) typically generate discrete text tokens.
arXiv Machine Learning
Jul 1
Is Natural Always Appropriate? Investigating Naturalness and Appropriateness Across Different Domains for TTS Evaluation
arXiv:2606. 31729v1 Announce Type: cross Abstract: Text-to-speech (TTS) evaluation is an open challenge.
Hugging Face Trending Papers
Jun 2
Efficient ASR Training with Conversations that Never Happened
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations.
arXiv AI
Jul 7
Unified Audio Intelligence Without Regressing on Text Intelligence
arXiv:2607. 05196v1 Announce Type: cross Abstract: Audio intelligence involves understanding, reasoning about, and generating both audio and speech.