Speech & Audio - Weekly Roundup

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.

Results show that targeted preprocessing, speaker selection, and domain-aware post-processing can substantially improve agricultural speech transcription while preserving the underlying ASR model.

Decoupled-Live-ProsodyJudge (D-LPJ), a cost-effective pairwise evaluator distilled from Gemini into Qwen3-Omni, and a novel span-local GRPO strategy that applies normalized advantages strictly to their corresponding rationale spans.

A discriminative adaptation that reads the final prompt token's hidden state through a classification head, producing a label in one forward pass without modifying the backbone, gives a controlled comparison of generative and discriminative inference in an otherwise identical speechLLM.

V\={a}kQA, a Telugu SQA benchmark of 2,001 factoid question-answer pairs across six domains, is introduced and it is observed that Telugu phrasing retains cultural specificity that is lost in translation, speech input introduces phonetic confusions that alter question meaning, and cascaded ASR-MT errors compound progressively.

Age, digital literacy, and interaction modality as correlates of psychomotor and cognitive-flexibility performance in immersive virtual reality (VR) are examined and age was associated with slower task times across modes, but controller direct touch carried the steepest relative age gradient.

Adaptive Score-based Routing Balancer is introduced, a dynamic, score-based request routing mechanism for Kubernetes-based service deployments over the computing continuum that jointly considers infrastructure-level information, response time measurements, and application-level quality indicators, with a particular focus on serving Machine Learning workloads.

The analysis separates the contributions of initial conditions, fast-timescale tracking error, Markovian dependence, and timescale coupling, thereby clarifying the origin of the $\beta^2/\alpha^2$ term.

The Multi-Turn Voice Agent Benchmark (MTVA-Bench), which evaluates the language model on the same conditions it faces inside a cascaded system, and contains 49 agents working across 490 reviewed scenarios and supports 7 languages.

This paper presents a context-aware neural G2P method that scores paths of a discriminative conditional random field over a word lattice constructed from dictionaries that strongly outperforms conventional morphological analyzer-based methods and neural sequence models.

This work presents a multimodal conversational-context framework for LLM-based ASR that integrates a scenario-controlled data pipeline, scalable multimodal context training, and systematic evaluation and introduces MM-ContextASR Bench, which evaluates contextual understanding and entity error correction across five scenarios.

An alignment-path distillation framework for streaming automatic speech recognition (ASR) with large language models (LLMs) that extracts monotonic alignment paths from a non-streaming teacher's soft text-audio attention and uses them to construct interleaved training sequences.

This paper introduces PersianVox, a fully automated pipeline designed to generate high-quality speech corpora from unlabeled web data, using a novel prosody-aware segmentation strategy that utilizes acoustic turn-detection to preserve linguistic completeness and optimize utterance duration for long-context modeling.

HearInContext, a Mandarin--English benchmark that pairs shared synthetic speech with assistant replies supporting different interpretations, is introduced, a Mandarin--English benchmark that pairs shared synthetic speech with assistant replies supporting different interpretations.

DUPAR is proposed, a conversational retrieval framework with complementary slow and fast paths that significantly reduces retrieval errors when the previous turn retrieves correct evidence and the follow-up targets a one-hop neighboring chunk.

Backend delegation is an effective and modular approach for combining natural duplex speech interaction with strong agentic tool-call capabilities and significantly outperforms GPT-realtime-mini and Qwen3-Omni-30B-A3B-Instruct on EVA-Bench.

AUDITPLAN is a single-model plan-then-answer approach where the model first emits a compact structured safety plan and then answers conditioned on it, enabling machine-checkable auditing while remaining hidden from users at deployment.

A multi-teacher knowledge distillation framework to train a lightweight, streaming content encoder that generalizes across healthy (HE) and EL speech that reduces the EL word error rate to 21.2%, compared to 39.3% for the strongest zero-shot SSL baseline.

AWED, a word-level emission-delay metric defined relative to the acoustic end of each word, is introduced and post-train a DSM recognizer with GRPO under a reward that scores transcription accuracy and measured delay jointly, advancing the accuracy--latency Pareto frontier of streaming ASR without architectural change.

This work presents Encypher, a collaborative generative music system that translates collective movement qualities into text prompts conditioning real-time music generation for dance cyphers, and offers a framework and design implications for AI systems for collaborative, embodied expression.

Encoder Awakening via Adapters (EAVA), a simple yet effective domain-adaptive fine-tuning method for Speech-LLM-based ASR, consistently outperforms vanilla fine-tuning and other baselines, achieving new state-of-the-art performance.