The increasing use of children’s automatic speech recognition (ASR) systems has spurred research efforts to improve the accuracy of models designed for children’s speech in recent years. The current approach utilizes either open-source speech foundation models (SFMs) directly or fine-tuning them with children’s speech data. These SFMs, whether open-source or fine-tuned for children, often exhibit higher word error rates (WERs) compared to adult speech. However, there is a lack of systemic analysis of the cause of this degraded performance of SFMs. Understanding and addressing the reasons behind this performance disparity is crucial for improving the accuracy of SFMs for children’s speech. Our study addresses this gap by investigating the causes of accuracy degradation and the primary contributors to WER in children’s speech. In the first part of the study, we conduct a comprehensive benchmarking study on two self-supervised SFMs ( Wav2Vec2.0 and Hubert ) and two weakly supervised SFMs ( Whisper and Massively Multilingual Speech (MMS) ) across various age groups on two children speech corpora, establishing the raw data for the causal inference analysis in the second part. In the second part of the study, we analyze the impact of physiological factors (age, gender), cognitive factors (pronunciation ability), and external factors (vocabulary difficulty, background noise, and word count) on SFM accuracy in children’s speech using causal inference. The results indicate that physiology (age) and particular external factor (number of words in audio) have the highest impact on accuracy, followed by background noise and pronunciation ability. Fine-tuning SFMs on children’s speech reduces sensitivity to physiological and cognitive factors, while sensitivity to the number of words in audio persists.

ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce the ASVspoof 5 database which is generated in a crowdsourced fashion from data collected in diverse acoustic conditions (cf. studio-quality data for earlier ASVspoof databases) and from ∼ 2,000 speakers (cf. ∼ 100 earlier). The database contains attacks generated with 32 different algorithms, also crowdsourced, and optimised to varying degrees using new surrogate detection models. Among them are attacks generated with a mix of legacy and contemporary text-to-speech synthesis and voice conversion models, in addition to adversarial attacks which are incorporated for the first time. ASVspoof 5 protocols comprise seven speaker-disjoint partitions. They include two distinct partitions for the training of different sets of attack models, two more for the development and evaluation of surrogate detection models, and then three additional partitions which comprise the ASVspoof 5 training, development and evaluation sets. An auxiliary set of data collected from an additional 30k speakers can also be used to train speaker encoders for the implementation of attack algorithms. Also described herein is an experimental validation of the new ASVspoof 5 database using a set of automatic speaker verification and spoof/deepfake baseline detectors. With the exception of protocols and tools for the generation of spoofed/deepfake speech, the resources described in this paper, already used by participants of the ASVspoof 5 challenge in 2024, are now all freely available to the community.

A key research area in deepfake speech detection is source tracing - determining the origin of synthesised utterances. The approaches may involve identifying the acoustic model (AM), vocoder model (VM), or other generation-specific parameters. However, progress is limited by the lack of a dedicated, systematically curated dataset. To address this, we introduce STOPA, a systematically varied and metadata-rich dataset for deepfake speech source tracing, covering 8 AMs, 6 VMs, and diverse parameter settings across 700k samples from 13 distinct synthesisers. Unlike existing datasets, which often feature limited variation or sparse metadata, STOPA provides a systematically controlled framework covering a broader range of generative factors, such as the choice of the vocoder model, acoustic model, or pretrained weights, ensuring higher attribution reliability. This control improves attribution accuracy, aiding forensic analysis, deepfake detection, and generative model transparency.

Training a robust Automatic Speech Recognition (ASR) system for children's speech recognition is a challenging task due to inherent differences in acoustic attributes of adult and child speech and scarcity of publicly available children's speech dataset. In this paper, a novel segmental spectrum warping and perturbations in formant energy are introduced, to generate a children-like speech spectrum from that of an adult's speech spectrum. Then, this modified adult spectrum is used as augmented data to improve end-to-end ASR systems for children's speech recognition. The proposed data augmentation methods give 6.5% and 6.1% relative reduction in WER on children dev and test sets respectively, compared to the vocal tract length perturbation (VTLP) baseline system trained on Librispeech 100 hours adult speech dataset. When children's speech data is added in training with Librispeech set, it gives a 3.7 % and 5.1% relative reduction in WER, compared to the VTLP baseline system.