Cameron Churchwell

FAC-FACodec: Controllable Zero-Shot Foreign Accent Conversion with Factorized Speech Codec

Yurii Halychanskyi, Cameron Churchwell, Yutong Wen, Volodymyr Kindratenko

Previous accent conversion (AC) methods, including foreign accent conversion (FAC), lack explicit control over the degree of modification. Because accent modification can alter the perceived speaker identity, balancing conversion strength and identity preservation is crucial. We present an AC framework that provides …

us (code pays fourni par la source)

That's Deprecated! Understanding, Detecting, and Steering Knowledge Conflicts in Language Models for Code Generation

Jae‐Sung Bae, Cameron Churchwell, Mitchell Hermon, Tsun-An Hsieh et autres

This paper investigates how large language models (LLMs) behave when faced with discrepancies between their parametric knowledge and conflicting information contained in a prompt. Building on prior question-answering (QA) research, we extend the investigation of knowledge conflicts to the realm of code …

Combolutional Neural Networks

Cameron Churchwell, Minje Kim, Paris Smaragdis

Selecting appropriate inductive biases is an essential step in the design of machine learning models, especially when working with audio, where even short clips may contain millions of samples. To this end, we propose the combolutional layer: a learned-delay IIR comb filter …

us (code pays fourni par la source)

Fine-Grained and Interpretable Neural Speech Editing

Max Morrison, Cameron Churchwell, Nathan Pruyne, Bryan Pardo

Fine-Grained and Interpretable Neural Speech Editing

Max Morrison, Cameron Churchwell, Nathan Pruyne, Bryan Pardo

Fine-grained editing of speech attributes$\unicode{x2014}$such as prosody (i.e., the pitch, loudness, and phoneme durations), pronunciation, speaker identity, and formants$\unicode{x2014}$is useful for fine-tuning and fixing imperfections in human and AI-generated speech recordings for creation of podcasts, film dialogue, and video game dialogue. Existing …

High-Fidelity Neural Phonetic Posteriorgrams

Cameron Churchwell, Max Morrison, Bryan Pardo

A phonetic posteriorgram (PPG) is a time-varying categorical distribution over acoustic units of speech (e.g., phonemes). PPGs are a popular representation in speech generation due to their ability to disentangle pronunciation features from speaker identity, allowing accurate reconstruction of pronunciation (e.g., voice …

us (code pays fourni par la source)

High-Fidelity Neural Phonetic Posteriorgrams

Cameron Churchwell, Max Morrison, Bryan Pardo

A phonetic posteriorgram (PPG) is a time-varying categorical distribution over acoustic units of speech (e.g., phonemes). PPGs are a popular representation in speech generation due to their ability to disentangle pronunciation features from speaker identity, allowing accurate reconstruction of pronunciation (e.g., voice …