# Speaker Identification from emotional and noisy speech data using learned voice segregation and Speech VGG

arXiv: https://arxiv.org/abs/2210.12701 · HTML: https://mlsanity.com/paper/2210.12701/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [ML-ASPA: A Contemplation of Machine Learning-based Acoustic Signal Processing Analysis for Sounds, & Strains Emerging Technology](https://mlsanity.com/paper/2402.10005/) (2023)
- [Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications](https://mlsanity.com/paper/2601.02432/) (2026)

## Abstract (arXiv)

Speech signals are subjected to more acoustic interference and emotional factors than other signals. Noisy emotion-riddled speech data is a challenge for real-time speech processing applications. It is essential to find an effective way to segregate the dominant signal from other external influences. An ideal system should have the capacity to accurately recognize required auditory events from a complex scene taken in an unfavorable situation. This paper proposes a novel approach to speaker identification in unfavorable conditions such as emotion and interference using a pre-trained Deep Neural Network mask and speech VGG. The proposed model obtained superior performance over the recent literature in English and Arabic emotional speech data and reported an average speaker identification rate of 85.2%, 87.0%, and 86.6% using the Ryerson audio-visual dataset (RAVDESS), speech under simulated and actual stress (SUSAS) dataset and Emirati-accented Speech dataset (ESD) respectively.