arXiv subjects

Kris Y. Hong

Publications and source records attributed to Kris Y. Hong.

3 recordsLinked to original sources

arXiv2026

AlignDPO: Preference-Gated Alignment for Reducing Hallucination in Decoder-Only TTS

Decoder-only text-to-speech (TTS) models scale efficiently but remain prone to content hallucinations that arise from weak text-speech alignment during autoregressive generation. We find that robustness is governed by a non-monotone relation to the sharpness of the alignment-bearing attention heads: a moderate degree is best, whereas over-sharpening is no better than the unaligned backbone and even less robust. Guided by this, we present AlignDPO, a post-training method that reaches this moderate regime by folding a lightweight connectionist-temporal-classification (CTC) alignment term into Direct Preference Optimization (DPO), applied only to the chosen samples, with no architectural or inference-time change. On the Seed-TTS-Eval English set, this significantly reduces the content-hallucination and word error rates relative to a strong DPO baseline and lowers the severe content-hallucination rate to ~0.6% (from 4.4%); a listening study further finds it preferred for naturalness over both the backbone and that baseline. Alignment is thus best learned and kept moderate rather than maximized or imposed at decoding. Audio samples are available at https://align-dpo-demo.vercel.app.

eess.AS↗

arXiv2025

Whilter: A Whisper-based Data Filter for "In-the-Wild" Speech Corpora Using Utterance-level Multi-Task Classification

Large-scale in-the-wild speech datasets have become more prevalent in recent years due to increased interest in models that can learn useful features from unlabelled data for tasks such as speech recognition or synthesis. These datasets often contain undesirable features, such as multiple speakers, non-target languages, and music, which may impact model learning. The Whilter model is proposed as a multitask solution to identify these undesirable samples. Whilter uses a Whisper encoder with an attention-based classifier to solve five diverse classification problems at once. In addition, an annotated dataset is published for a subset of two popular in-the-wild corpora. Whilter achieves F1 scores above 85% and equal error rates of 6.5% to 7.8% for three of five subtasks, outperforming a state-of-the-art BEATs classifier on speech-specific classes, with a notable decrease in processing time compared to a combination of single-task alternatives.

cs.SD↗

arXiv2019

Predicting Global Variations in Outdoor PM2.5 Concentrations using Satellite Images and Deep Convolutional Neural Networks

Here we present a new method of estimating global variations in outdoor PM$_{2.5}$ concentrations using satellite images combined with ground-level measurements and deep convolutional neural networks. Specifically, new deep learning models were trained over the global PM$_{2.5}$ concentration range ($<$1-436 $μ$g/m$^3$) using a large database of satellite images paired with ground level PM$_{2.5}$ measurements available from the World Health Organization. Final model selection was based on a systematic evaluation of well-known architectures for the convolutional base including InceptionV3, Xception, and VGG16. The Xception architecture performed best and the final global model had a root mean square error (RMSE) value of 13.01 $μ$g/m$^3$ (R$^2$=0.75) in the disjoint test set. The predictive performance of our new global model (called IMAGE-PM$_{2.5}$) is similar to the current state-of-the-art model used in the Global Burden of Disease study but relies only on satellite images as input. As a result, the IMAGE-PM$_{2.5}$ model offers a fast, cost-effective means of estimating global variations in long-term average PM$_{2.5}$ concentrations and may be particularly useful for regions without ground monitoring data or detailed emissions inventories. The IMAGE-PM$_{2.5}$ model can be used as a stand-alone method of global exposure estimation or incorporated into more complex hierarchical model structures.

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