arXiv subjects

Patrick Aichroth

Publications and source records attributed to Patrick Aichroth.

6 recordsLinked to original sources

arXiv2026

Neural Encoding Detection is Not All You Need for Synthetic Speech Detection

This paper reviews the current state and emerging trends in synthetic speech detection. It outlines the main data-driven approaches, discusses the advantages and drawbacks of focusing future research solely on neural encoding detection, and offers recommendations for promising research directions. Unlike works that introduce new detection methods or datasets, this paper aims to guide future state-of-the-art research in the field and to highlight the risk of overcommitting to approaches that may not stand the test of time.

eess.AS↗

arXiv2026

Multi-Task Transformer for Explainable Speech Deepfake Detection via Formant Modeling

In this work, we introduce a multi-task transformer for speech deepfake detection, capable of predicting formant trajectories and voicing patterns over time, ultimately classifying speech as real or fake, and highlighting whether its decisions rely more on voiced or unvoiced regions. Building on a prior speaker-formant transformer architecture, we streamline the model with an improved input segmentation strategy, redesign the decoding process, and integrate built-in explainability. Compared to the baseline, our model requires fewer parameters, trains faster, and provides better interpretability, without sacrificing prediction performance.

cs.SD↗

arXiv2022

Environment Classification via Blind Roomprints Estimation

In this paper we present a novel approach for environment classification for speech recordings, which does not require the selection of decaying reverberation tails. It is based on a multi-band RT60 analysis of blind channel estimates and achieves an accuracy of up to 93.6% on test recordings derived from the ACE corpus.

eess.AS↗

arXiv2022

Open Challenges in Synthetic Speech Detection

In this paper the current status and open challenges of synthetic speech detection are addressed. The work comprises an initial analysis of available open datasets and of existing detection methods, a description of the requirements for new research datasets compliant with regulations and better representing real-case scenarios, and a discussion of the desired characteristics of future trustworthy detection methods in terms of both functional and non-functional requirements. Compared to other works, based on specific detection solutions or presenting single dataset of synthetic speeches, our paper is meant to orient future state-of-the-art research in the domain, to quickly lessen the current gap between synthesis and detection approaches.

eess.AS↗

arXiv2022

Speaker-Independent Microphone Identification in Noisy Conditions

This work proposes a method for source device identification from speech recordings that applies neural-network-based denoising, to mitigate the impact of counter-forensics attacks using noise injection. The method is evaluated by comparing the impact of denoising on three state-of-the-art features for microphone classification, determining their discriminating power with and without denoising being applied. The proposed framework achieves a significant performance increase for noisy material, and more generally, validates the usefulness of applying denoising prior to device identification for noisy recordings.

eess.AS↗

arXiv2019

Text Classification Components for Detecting Descriptions and Names of CAD models

We apply text analysis approaches for a specialized search engine for 3D CAD models and associated products. The main goals are to distinguish between actual product descriptions and other text on a website, as well as to decide whether a given text is or contains a product name. For this we use paragraph vectors for text classification, a character-level long short-term memory network (LSTM) for a single word classification and an LSTM tagger based on word embeddings for detecting product names within sentences. Despite the need to collect bigger datasets in our specific problem domain, the first results are promising and partially fit for production use.

cs.IR↗