Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2207.01963

Relating the fundamental frequency of speech with EEG using a dilated convolutional network

Corentin Puffay·Jana Van Canneyt·Jonas Vanthornhout·Hugo Van Hamme·Tom Francart
View original source ↗Read source document ↗Export metadata ↓

Abstract

To investigate how speech is processed in the brain, we can model the relation between features of a natural speech signal and the corresponding recorded electroencephalogram (EEG). Usually, linear models are used in regression tasks. Either EEG is predicted, or speech is reconstructed, and the correlation between predicted and actual signal is used to measure the brain's decoding ability. However, given the nonlinear nature of the brain, the modeling ability of linear models is limited. Recent studies introduced nonlinear models to relate the speech envelope to EEG. We set out to include other features of speech that are not coded in the envelope, notably the fundamental frequency of the voice (f0). F0 is a higher-frequency feature primarily coded at the brainstem to midbrain level. We present a dilated-convolutional model to provide evidence of neural tracking of the f0. We show that a combination of f0 and the speech envelope improves the performance of a state-of-the-art envelope-based model. This suggests the dilated-convolutional model can extract non-redundant information from both f0 and the envelope. We also show the ability of the dilated-convolutional model to generalize to subjects not included during training. This latter finding will accelerate f0-based hearing diagnosis.

Explore related subjects

eess.AS

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Corentin Puffay, Jana Van Canneyt, Jonas Vanthornhout, Hugo Van Hamme, Tom Francart. 2022-07-05. Relating the fundamental frequency of speech with EEG using a dilated convolutional network. https://arxiv.org/abs/2207.01963

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Recovering the Zipfian Distribution in Unsupervised Term Discovery

Unsupervised term discovery involves segmenting unlabelled speech into word- or syllable-like units and clustering these into a lexicon of candidate types. True lexicons follow a Zipfian distribution, yet the dominant centre-based clustering approach -- K-means -- produces a more uniform distribution due to an inductive bias toward spherical clusters. In this paper we revisit graph-based clustering as a bottom-up alternative, where segment embeddings are connected by pairwise similarity and partitioned using the Leiden algorithm. We show that graph clustering substantially outperforms centre-based approaches (K-means, GMM, BIRCH) in both word- and syllable-level lexicon discovery across three languages, producing more Zipf-like distributions. Another bottom-up approach, agglomerative clustering with average linkage, also performs well, although it is computationally less efficient and allows for less control over the resulting distribution. Our work calls into question the dominance of centre-based clustering for term discovery, and promotes graph clustering as an attractive alternative.

eess.AS↗
arXiv2026

Preserving Speech-to-Text LLM Capabilities in Speech-to-Speech Generation

Strong speech-to-text (S2T) LLMs already provide robust speech perception and text reasoning, but adding speech-to-speech (S2S) output is challenging: fine-tuning the backbone can degrade the original S2T performance, while attaching a downstream talker reintroduces a serial text-to-speech bottleneck. We present PRIME-Speech, a frozen-backbone S2S conversion framework that trains only speech-generation modules. PRIME-Speech synchronizes a causal audio post-decoder with intermediate hidden states of the frozen backbone, so codec tokens are generated from the model's evolving reasoning trajectory rather than from completed text chunks. The post-decoder uses mixed hidden-state, text, and audio-history conditioning, and a training-time packing strategy with turn-level audio KV-cache and position reset stabilizes multi-turn spoken interaction without additional multi-turn S2S training data. Multi-token prediction further reduces the effective codec prediction rate and improves first-audio latency without modifying the reasoning path. Across speech translation, spoken QA, speech understanding, and multi-turn dialogue, PRIME-Speech preserves the S2T behavior of the frozen backbone while producing accurate, low-WER spoken responses.

eess.AS↗
arXiv2026

SRF-SVB: Style-Consistent Singing Voice Beautifying via Rectified Flow

Singing voice beautifying (SVB) aims to correct pitch and rhythm of amateur singing while enhancing vocal quality, preserving lyrics and the singer's timbre. Existing methods, however, suffer from limited generation quality and efficiency, and tend to neglect the preservation of the singer's style. We propose SRF-SVB, a style-consistent model for SVB via rectified flow, which achieves high-fidelity and efficient beautification covering pitch and rhythm correction. Furthermore, we design a context-guided masked mel-spectrogram inpainting mechanism that effectively preserves the amateur singer's style, including unique timbre and expressive patterns. Experiments on both English and Chinese test sets show that SRF-SVB outperforms baseline models in most objective and subjective metrics.

eess.AS↗