In this study, we introduce ExpressiveSinger, an end-to-end ex-pressive singing voices synthesis model, which accurately re-flect users' musical expression by analyzing real-played MIDI sequences and lyrics. We propose a novel method to auto-matically annotate velocity labels for MIDI sequences in SVS datasets, as these sequences do not inherently contain velocity information compared to real-played MIDI sequences. More-over, we separately model expressive features and modify the vocoder to enhance controllability and quality of the synthetic singing voices. Finally, we adopt a soft-vc like approach for end-to-end training to effectively preserve more linguistic content features. Our experiments on the professional Mandarin singing corpus validate our data annotation method and demonstrate the effectiveness of ExpressiveSinger in terms of naturalness and a strong correlation between the synthetic singing voice and the MIDI input.
Auditory attention represents a key neural process in the brain's handling of auditory information, essential for effective human communication. Electroencephalography (EEG) has proven to be effective in auditory attention decoding (AAD). However, current methods primarily concentrate on individual subject settings, which fail to achieve robust models across subjects due to notable variations between subjects and noisy labels. To overcome these issues, this paper investigates a novel domain adaptation method using prototypical representation(DApro) to extract both specific and invariant representations across subjects. Specifically, we align the features from the source and target domains to a shared feature space, while considering the relationships with prototypical representations both within and between classes. Features of the same category are clustered together, while those of different categories are separated. The proposed method was evaluated on the publicly available AHU dataset using a leave-one-subject-out cross-validation protocol. The experimental results demonstrate the effectiveness and the superiority of our model in terms of generalization and robustness in the cross-subject auditory attention classification using EEG signals.
The exponential growth of academic papers presents a huge challenge for researchers, exacerbating the already tedious literature review process. Current tools like Google Scholar and Connected Papers offer solutions for text-based and citation-based searches but fail to address the need for finding semantically similar yet terminologically different papers efficiently. This paper proposes an innovative approach to paper discovery using semantic search to create a knowledge graph of topics and papers. By generating a tree of topics using ChatGPT 4o and calculating semantic similarity with SciBERT, this method aims to uncover relevant papers overlooked by traditional citation-based searches. The solution, validated through quantitative evaluation, demonstrates the potential to improve the efficiency and comprehensiveness of paper discovery.
Mandarin seven-sound test was introduced to bridge a frequency gap from 6000 Hz to 8000 Hz identified in the standard Mandarin version of the Ling six-sound test. However, until the present research, there are few relevant studies to confirm the feasibility of this test or to investigate its potential for use across various pronunciation communities. In this study, eight speakers were invited to record the audio material for Mandarin seven-sound test. Subsequently, 31 adult participants took the Mandarin seven-sound test using these recordings. The results showed that individuals with hearing loss performed significantly poorer than those with normal hearing in terms of both reaction time and accuracy rate. Specifically, within the hearing-loss group, the accuracy rates for the /s/ and /(sic)/ sounds were notably lower compared to other phonemes. Additionally, this study found that the demographic characteristics of the speakers, such as gender and age, did not significantly influence the accuracy rate for either group. These results indicate that Mandarin seven-sound test not only effectively addresses the frequency gap of the Ling six-sound test but also maintains a high level of consistency across different speakers. Moreover, the online testing system has proven to be strong user-friendliness and provides significant value for clinical speech audiometry, speech therapy, and the development of hearing aid technology.
In addition to affecting the segmental levels, speech rate can also influence suprasegmental acoustic features, such as rhythm. As an essential element of prosody, the rhythmic properties of Mandarin at different speech rates have been underexplored. This study aims to explore the impact of speech rate on the rhythmic characteristics of Mandarin Chinese in semi-spontaneous speech. Using speech materials from the CCTV news broadcast, the study employs a linear mixed-effects model to compare the differences in rhythmic patterns between normal and slow speech rates. The results indicate that speech rate has a statistically significant effect on Mandarin rhythm, both for vowels and consonants, with the values of rhythmic parameters being significantly higher under slow speech conditions compared to normal speech conditions. It suggests that Mandarin Chinese exhibits greater intra-syllabic and inter-syllabic variability in rhythm at slower speech rates. Reduced speech rate may enhance the expressiveness and complexity of Mandarin rhythm. This finding highlights the importance of considering speech rate in studies of Mandarin prosody.
