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.
One Mean Log-Mel Vector per Phoneme: Simple and Fast Text Injection for Speech Recognition
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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 10
WhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Small Pretrained Speech Recognition Transformers
arXiv:2509.10452v3 Announce Type: replace-cross Abstract: Pretrained automatic speech recognition (ASR) models such as Whisper perform well but still need domain adaptation to handle unseen parlance....
arXiv AI
Jul 15
Audio-Native Speech Recognition with a Frozen Discrete-Diffusion Language Model
arXiv:2607. 13013v1 Announce Type: new Abstract: Automatic speech recognition is dominated by autoregressive decoders that emit one token at a time.
arXiv AI
Oct 5
DriftTTS: Few-Step Text-to-Speech Without Distillation via Distribution-Matching Drift
DriftTTS is a few‑step neural text‑to‑speech model that generates mel‑spectrograms without relying on a generative teacher, distillation, or adversarial discrimination. It employs a distribution‑matching drift objective in a mel‑domain feature space defined by raw mels and a frozen masked‑autoencoder encoder pretrained on LJSpeech. On the LJSpeech dataset, DriftTTS achieves competitive metrics (3.87 dB MCD, 3.7% WER) and a MOS of 4.18, rivaling the Matcha‑TTS baseline and approaching ground‑truth quality.
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 Computation and Language
Sep 28
Training-Free Pronunciation Transcription via Text-Constrained Acoustic Rescoring
The paper introduces a training‑free speech‑and‑text‑to‑pronunciation (ST2P) pipeline that combines lexical candidates from G2P tools with acoustic rescoring using frozen pretrained S2P models. By performing a left‑to‑right greedy search over whole‑sequence negative log‑likelihoods, the method achieves a dramatic reduction in character error rate on Japanese corpora, outperforming both baseline G2P/S2P approaches and commercial multimodal LLMs. The approach is also significantly faster—3–3.5× faster than beam search and twice as fast as direct decoding—while maintaining high accuracy across multiple languages.
Hugging Face Trending Papers
Jul 14
Audio-Native Speech Recognition with a Frozen Discrete-Diffusion Language Model
Automatic speech recognition is dominated by autoregressive decoders that emit one token at a time. We ask whether a discrete diffusion language model can transcribe speech instead, refining a whole transcript in parallel over a small number of denoising steps.
arXiv Computation and Language
Oct 19
Do Speech BCIs Need Larger Models? Rethinking Neural Decoding beyond Scaling
The paper investigates whether large models are necessary for speech brain‑computer interfaces (BCIs) and introduces BEST, a two‑stage framework that uses hierarchical intermediate representations and a compact decoder to map neural activity directly to text. BEST achieves low word error rates (6.19% and 1.62%) on the Brain‑to‑Text ’24 and ’25 benchmarks while using only 3.85% of the parameters of comparable methods. The study also highlights that decoder scaling yields non‑monotonic gains, cross‑modal alignment is more critical than model size, and overfitting harms sequence‑level performance.
arXiv Computation and Language
Oct 7
DirectSpeech2LLM: A Simple End-to-End Framework to Mitigate Prompt Overfitting in Speech-LLMs
arXiv:2610.08085v1 Announce Type: new Abstract: Speech-LLMs often exhibit prompt overfitting, where models solely trained on automatic speech recognition (ASR) instruction fail to generalize to new i...
arXiv AI
Jun 8
dots.tts Technical Report
arXiv:2606. 07080v1 Announce Type: cross Abstract: We present dots.
arXiv Machine Learning
Jul 23
Cumsum-Composable Phase Transport for Low-Cost Streaming Keyword Spotting
arXiv:2607. 20086v1 Announce Type: cross Abstract: State-space sequence models are attractive for streaming speech because they maintain compact recurrent state, but scan-style training kernels can have unfavorable constants for short audio tasks.