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arXiv AI

Where Does Timbre Hide? Localizing Speaker Information in Speech Signals

The paper investigates where speaker timbre information is located within speech signals, examining frequency bands, phase/magnitude structure, source/filter decomposition, voiced/unvoiced regions, SSL layers, and codec tokens across Mandarin, English, and Japanese data. It finds that 1–8 kHz magnitude and vocal‑tract spectral‑envelope features dominate perception, while above‑8 kHz bands remain machine‑discriminative; shallow SSL layers reveal compact speaker cues, and token leakage varies with tokenizer and layer choice. These insights guide where generative speech systems should condition on or transform speaker information and where to audit residual leakage.

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arXiv AI
Sep 2

Heard but Not Heeded: Paralinguistic Information Encoding and Loss in Audio-Language Models

The paper investigates whether audio‑language models capture paralinguistic cues beyond spoken content. Using the Expresso dataset and four open‑source models, the authors trace how speaking style information is encoded in the late layers of the audio encoder but is degraded before reaching the final output. They find that some models are content‑driven while others are acoustic‑driven, revealing a gap between what is encoded and what is utilized in current audio‑language models.

By Bhuvan Koduru, Dareen Safar B Alharthi, Rita Singh, Bhiksha Raj
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arXiv Machine Learning
Sep 24

PHONOS: PHOnetic Neutralization for Online Streaming Applications

PHONOS is a real‑time streaming module for speaker anonymization that neutralizes accent cues by converting non‑native segmental realizations toward a target accent domain. It uses pre‑generated golden utterances that preserve timbre and rhythm, aligning them with silence‑aware DTW and applying zero‑shot voice conversion to supervise a causal accent translator. The system achieves an 81% reduction in non‑native accent confidence, improves accentedness ratings, reduces speaker linkability in embedding space, and operates with ≤241 ms end‑to‑end latency on a single GPU.

By Waris Quamer, Mu-Ruei Tseng, Ghady Nasrallah, Ricardo Gutierrez-Osuna
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arXiv Computation and Language
Sep 28

Asymmetric Classifier-Free Guidance for Target-Speaker ASR

The paper introduces asymmetric classifier‑free guidance (CFG) for target‑speaker ASR using Whisper, where a speaker‑conditioned branch predicts the target transcript and a speaker‑unconditioned branch predicts serialized multi‑speaker transcripts. CFG modulates the influence of speaker conditioning during decoding via a single guidance scale, which is first set globally on development data and then refined per utterance by a lightweight encoder‑based predictor while keeping the recognition model fixed. The resulting system yields up to 21.8% relative WER reduction over a condition‑only baseline and 5.6% over standard conditional decoding under domain shifts.

By Yiwen Guan, Jacob Whitehill
multimodal
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arXiv Machine Learning
Sep 29

A Comprehensive Study of Content Representations for Speech Synthesis

The paper investigates how different speech content representations—such as SSL features, supervised tokens, posteriorgrams, and neural audio codecs—perform when used to train a generative model that produces audio conditioned only on each representation. By evaluating the generated audio on content, speaker identity, and prosody, the study identifies two regimes: some representations almost fully reconstruct the original audio, while others effectively separate speaker identity. The findings reveal that disentanglement of speaker identity depends on both the training objective and the representation’s information capacity, rather than supervision alone.

By Diego Torres, Axel Roebel, Nicolas Obin
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