arXiv AI
Jul 9
Gradient-Based Speech-to-Text Alignment for Any ASR Model: From CTC to Speech LLMs
arXiv:2607. 06831v1 Announce Type: cross Abstract: Speech-to-text alignment means finding the temporal boundaries of each word in the audio.
Align Then Reason: A Multimodal Lip-Sync Judge for Dubbing
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arXiv Computation and Language
Sep 4
Alignment-Free Text-Audiobox for Voice Dubbing and Full-Duplex Dialogue Synthesis
The paper introduces Alignment-Free Text‑Audiobox (Text‑AB), a unified diffusion‑based framework that performs high‑quality voice dubbing and full‑duplex dialogue synthesis without requiring forced alignment. Text‑AB uses a latent diffusion model with DAC‑VAE features, achieving over 10× compression compared to prior EnCodec representations, and learns text‑speech alignment via cross‑attention. The authors pretrain a 3B‑parameter model on 480k hours of monolingual speech and fine‑tune it for cross‑lingual dubbing, full‑duplex dialogue, and emotional dialogue, reporting significant improvements in prosody, voice similarity, naturalness, and emotional expressivity over existing internal systems.
arXiv Computation and Language
Sep 10
StreamAlign: Streaming Text-Aligned Speech Tokenization
arXiv:2609.09719v1 Announce Type: new Abstract: Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretr...
arXiv AI
Jun 2
Temporally-Aligned Evaluation for Audio-Driven Talking Head Generation
arXiv:2606. 01031v1 Announce Type: cross Abstract: Audio-driven talking-head generation has advanced rapidly, yet existing evaluation protocols mainly rely on frame-wise metrics that assume strict temporal correspondence between generated and reference videos.
arXiv AI
Jun 3
AnyAudio-Judge: A Dynamic Rubric-Based Benchmark and Evaluator for Audio Instruction Following
arXiv:2606. 03116v1 Announce Type: cross Abstract: The rapid advancement of instruction-guided audio generation has highlighted the critical need for robust alignment evaluation.
Hugging Face Trending Papers
Aug 12
Confucius4-TTS: Transcript-Free Cross-Lingual Zero-Shot TTS with a Learnable Speaker Encoder
Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time.
arXiv Machine Learning
Sep 18
Enabling automatic transcription of child-centered audio recordings from real-world environments
The paper presents a method to automatically identify utterances in child-centered daylong audio recordings that can be reliably transcribed by modern ASR systems, enabling accurate transcription of a substantial portion of the speech. On four English corpora, the approach achieves a median WER of 0% and a mean WER of 16% when transcribing 30% of the total speech, compared to a median WER of 52% when transcribing all speech. Word frequency distributions from the automatic transcripts correlate strongly with manual annotations (r = 0.94 overall, r = 0.99 for frequent words).
arXiv Computation and Language
Aug 31
Phoneme- and Word-Level Metrics Using Self-Supervised Speech Representations for Forced Alignment Evaluation
The paper introduces two new corpus‑level, reference‑free metrics—Phoneme‑Cluster Mutual Information (PCMI) and Word Acoustic Consistency Score (WACS)—that use self‑supervised speech representations to evaluate forced alignment quality. PCMI quantifies how well aligned phoneme labels agree with clusters derived from SSL representations, while WACS assesses consistency across repeated word realizations via dynamic time warping of word representation sequences. Experiments on 85 languages from FLEURS and 45 languages in DoReCo show that both metrics degrade predictably under alignment perturbations, effectively distinguish high‑ from low‑quality alignments, and correlate strongly with traditional timestamp‑based measures, enabling scalable, multilingual alignment evaluation without manual annotations.
arXiv Computer Vision
Sep 10
AVSRBench: A Multi-Condition AVSR Benchmark
arXiv:2609.10366v1 Announce Type: cross Abstract: While AVSR has achieved sub-1% word error rates on the standard LRS3 benchmark, its reliance on broadcast speech obscures whether this reflects true...
arXiv Computation and Language
Sep 25
Personalized Korean Lipreading as Visual Speech Recognition: Transfer, Census and Adaptation on OLKAVS
The paper introduces a personalized Korean visual speech recognition system that uses a video-only Conformer model initialized from English-trained weights, achieving a character error rate (CER) of 9.95–12.19% on the OLKAVS nine-camera corpus and 19.00–21.52% on unseen wording. Individual speaker CER varies widely (1.0–52.2%), with seen wording reducing errors by 7.0–9.0 points and professional or spontaneous speech increasing errors by 8.5–12.7 points. A low‑rank adapter, comprising only 4.6% of the model parameters and trained on 4–29 minutes of a user’s frontal video, reduces high‑error speakers’ CER by 2.13–3.58 points, transfers across all cameras without loss, and retains 85% of full fine‑tuning benefits at 12% of its cost; cameras above the mouth plane add a constant offset of about six CER points that can be mitigated by training on all views.
arXiv Computer Vision
Sep 3
From Visual Cues to Spoken Narration: Rethinking Audio Description
The paper introduces Cue2Narrate, a two‑stage pipeline that jointly predicts what visual events to narrate and when to insert the narration in long, untrimmed movie clips. It uses a dual‑head audio‑visual localizer to identify visual cue and narration windows, followed by a LoRA‑adapted vision‑language model that generates concise audio descriptions, trained with a Description Ranking Loss. The authors also present the LongLSMDC benchmark, comprising up to 8‑minute clips, and show that Cue2Narrate outperforms video‑only and audio‑only baselines by 5–12 points in average mAP and improves AD generation over fine‑tuned base VLMs.
Hugging Face Trending Papers
Sep 1
From Visual Cues to Spoken Narration: Rethinking Audio Description
Audio Description (AD) provides spoken narration of visual events during dialogue gaps, making movies accessible to visually impaired audiences. The problem requires determining both what (which visua...