Two Papers Accepted at APSIPA 2025 in Singapore

I’m thrilled to share that two of my papers have been accepted for presentation at the APSIPA Annual Summit and Conference 2025, taking place in vibrant Singapore this October. This marks a significant milestone for our research team and underscores our ongoing commitment to advancing speech and audio processing, particularly for health applications.

Accepted Papers

Paper ID	Title
120	Dementia Prediction From Speech Signal Using Optimized Prosodic Features
241	Comparison of Solicited and Longitudinal Cough Sounds for Tuberculosis Detection

Paper Summaries

Dementia Prediction From Speech Signal Using Optimized Prosodic Features

This study explores how subtle changes in speech prosody—such as pitch, rhythm, and intensity—can serve as early indicators of dementia. By optimizing feature selection and leveraging machine learning classifiers, our approach achieved a classification accuracy that outperforms several baseline models. We believe this work could pave the way for noninvasive, cost-effective screening tools.

Comparison of Solicited and Longitudinal Cough Sounds for Tuberculosis Detection

In this paper, we examine the diagnostic power of cough sound recordings collected under controlled (“solicited”) versus naturalistic (“longitudinal”) conditions. Our analysis demonstrates that longitudinal data, captured through everyday smartphone use, retains enough acoustic signatures to reliably flag tuberculosis. The findings suggest a path toward scalable, remote health monitoring in resource-limited settings.

Acknowledgements

• My co-authors and lab mates for their relentless dedication to data collection and algorithm development
• Funding agencies and institutional support that made this research possible
• All participants who shared their speech and cough recordings, enabling us to push the boundaries of health diagnostics

Next Steps

1. Prepare camera-ready manuscripts and finalize supplementary materials
2. Coordinate travel plans and poster backdrops for Singapore
3. Schedule rehearsals for the oral presentations
4. Network with fellow APSIPA attendees to explore collaborations in speech-based health analytics

I look forward to sharing our findings with the APSIPA community and gathering feedback that will fuel the next phase of our research. See you in Singapore!

A paper was accepted at ICAIIC 2025!

Alhamdulillah, our paper has been accepted for the conference ICAIIC 2025. This paper discusses the importance of ensemble learning to improve speech classification accuracy. We proposed performance-weighting methods to evaluate with two variants: using weighted and unweighted accuracies.

Our work is highly beneficial to society, as it will help to improve the performance of speech classification. It may also be generalizable to other domains outside of the speech area.

Several aspects can still be developed further, such as incorporating other weighting methods, and implementation in other datasets as well as in other tasks. We invite readers to collaborate in addressing the unresolved questions above.

We extend our gratitude to AIST for their full support of our research, NEDO and JST for research funding.

Happy reading. We welcome your feedback. See you in Fukuoka!

URL for downloading the paper: (will be given after it is available or contact me to get the accepted version).

Mencoba TTS Bahasa Indonesia dengan VITs dan Meta MMS

Sudah lama saya ingin mencoba (membuat) teknologi text-to-speech (TTS) atau speech synthesis bahasa Indonesia. Percobaan pertama saya beberapa tahun lalu gagal. Disini reponya: Expressive-FastSpeech2. Pada percobaan tersebut, saya langsung mencoba membuat suara (bukan Bahasa Indonesia) yang memiliki emosi, seperti suara orang marah, sedih, atau senang. Alih-alih suara, saya hanya mendengar derau/bising saja dari algoritma FastSpeech2.

Ketika Meta/Facebook mengumumkan salah satu riset mereka, yakni MMS (Massively Multilingual Speech), saya langsung tertarik mencobanya. MMS bisa diaplikasikan untuk ASR (automatic speech recognition, atau STT, speech-to-text) dan TTS. Untuk TTS, sepemahaman saya, Meta hanya mengaplikasikan dataset yang besar pada Variational Inference with adversarial learning for end-to-end Text-to-Speech (VITS). Saya coba ASRnya tidak lebih baik dari OpenAI Whisper, khususnya dari sisi latency. Saya coba TTSnya, kebetulan hasilnya memuaskan, khususnya untuk yang belum pernah berhasil membuat TTS sendiri.

Repository

Untuk keperluan TTS ini, saya buat repository khusus di Github: TTS-Bahasa. TTS-Bahasa sebenarnya tidak khusus untuk bahasa Indonesia, tapi semua bahasa yang didukung oleh MMS (ada lebih dari 1000 bahasa). Repo tersebut saya adaptasi dari tutorial di laman MMS, yakni tutorial Google Colabnya. Saya hanya menambahkan satu skrip python CLI (command line interface) untuk memudahkan pembuatan audio file suara sintesis berdasarkan input kalimat. Contohnya seperti ini.

python3 mms_tts_ind.py --text "Selamat datang di Indonesia"

Suara berbahasa Indonesia akan diperdengarkan setelah eksekusi program selesai (berbunyi: "Selamat datang di Indonesia"). Luaran suara tersebut juga bisa disimpan dalam format WAV atau MP3, misalnya.

python3 mms_tts_ind.py --text "Selamat datang di Indonesia" -s -o selamat_datang.wav 

Untuk mencobanya, tidak perlu menginstall. Cukup clone repo tersebut, dan ikuti petunjuk di READMEnya. Jika ada kendala, anda bisa membuka "issues" di repo tersebut.

Demo

Jika anda bukan programmer, coder, researcher, mahasiswa teknik, atau tidak terbiasa dengan Python, anda bisa langsung mencoba demo-nya disini: https://bagustris.github.io/tts-bahasa/.

New Paper: Speech Naturalness Recognition

Abstract

This study proposes an automatic naturalness recognition from an acted dialogue. The problem can be stated that: given speech utterances with their naturalness labels, is it possible to recognize these labels automatically? By what methods? And how to evaluate these methods? We evaluated two supervised classifiers to investigate the possibility of recognizing naturalness automatically in acted speech: long short-term memory and multilayer perceptron neural networks. These classifiers accept inputs in the form of acoustic features from a speech dataset. Two kinds of acoustic features were evaluated: low-level and high-level features. This initial study on automatic naturalness recognition of speech resulted in a moderate performance of the assessed systems. We measured the performance in concordance correlation coefficients, Pearson correlation coefficients, and root mean square errors. This study opens a potential application of speech processing techniques for measuring naturalness in acted dialogue, which benefits for drama- or movie-making in the future.

Illustration (of potential application):

(best) Result

Metric: concordance + Pearson correlation coefficient (CCC, PCC), [root] mean square error ([R]MSE)

Method; Multilayer perceptron (MLP) with high-level statistical functions (HSF)

Interpretation: intermediate result (CCC)

Full paper + Code

https://github.com/bagustris/snr