Speech & Audio - Weekly Roundup

Control accent strength in zero-shot TTS with a single model and inference-time weight, outperforming cascaded systems on out-of-domain accents without sacrificing speaker identity.

Control accent strength in zero-shot TTS with a single model and inference-time weight, outperforming cascaded systems on out-of-domain accents without sacrificing speaker identity.

Timbre drift in multi-turn expressive voice agents can be eliminated by replacing implicit context embeddings with explicitly supervised style instructions optimized via rejection sampling and context-aware DPO.

TFA is introduced, a reliability-guided, training-free aggregation method requiring neither place labels nor task-specific weight updates that can recover additional retrieval capability from frozen representations, providing a practical baseline for new environments with scarce place supervision.

It is concluded that cross-paper ASR comparison is currently unsupported, and a ten-item reporting checklist targeted at each failure is proposed, to supply the shared measurement contract the field has so far done without.

StreamTN is proposed, a lightweight LLM-based Chinese streaming TN model that employs a dual-track streaming framework in which input tokens and output tokens are processed on two parallel tracks, enabling low-latency real-time inference without complex prompting.

This work training a LALM with reinforcement learning to reason over its own speech output generates a draft speech as a form of audio-token reasoning, critiques its own generation by reflecting on the acoustic realization in text, and produces a refined version conditioned on both the first-pass speech and the critique.

This work proposes replacing an empty representation with a learnable unconditional embedding, optimized to represent a meaningful unconditional state, and shows that learnable null embeddings consistently outperform fixed null embeddings across speaker similarity, speech stability, and expressiveness, while exhibiting greater robustness to larger guidance scales.

This work proposes an end-to-end text-to-speech model that augments the text encoding mechanism with a morpho-syntactic prior and replaces the standard phoneme encoder in the VITS architecture with a morpheme sequence encoder and a phoneme-to-morpheme attention network.

AURA is proposed, an ultra-efficient representation-editing method that freezes the pretrained model and applies sparse scale-and-shift edits to decoder cross-attention heads, supporting dynamic activation editing as a practical path for grounding AED speech models.