One Mean Log-Mel Vector per Phoneme: Simple and Fast Text Injection for Speech Recognition
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arXiv:2607. 06831v1 Announce Type: cross Abstract: Speech-to-text alignment means finding the temporal boundaries of each word in the audio.
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: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:2607. 13013v1 Announce Type: new Abstract: Automatic speech recognition is dominated by autoregressive decoders that emit one token at a time.
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: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...