# Frame-based overlapping speech detection using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2001.09937 · HTML: https://mlsanity.com/paper/2001.09937/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Network](https://mlsanity.com/paper/2104.02878/) (2021)
- [Real-time Speaker counting in a cocktail party scenario using Attention-guided Convolutional Neural Network](https://mlsanity.com/paper/2111.00316/) (2021)
- [Speaker conditioning of acoustic models using affine transformation for multi-speaker speech recognition](https://mlsanity.com/paper/2111.00320/) (2021)
- [Single-channel speech separation using Soft-minimum Permutation Invariant Training](https://mlsanity.com/paper/2111.08635/) (2021)
- [LSTM-CNN Network for Audio Signature Analysis in Noisy Environments](https://mlsanity.com/paper/2312.07059/) (2023)
- [Joint Speech Activity and Overlap Detection with Multi-Exit Architecture](https://mlsanity.com/paper/2209.11906/) (2022)

## Abstract (arXiv)

Naturalistic speech recordings usually contain speech signals from multiple speakers. This phenomenon can degrade the performance of speech technologies due to the complexity of tracing and recognizing individual speakers. In this study, we investigate the detection of overlapping speech on segments as short as 25 ms using Convolutional Neural Networks. We evaluate the detection performance using different spectral features, and show that pyknogram features outperforms other commonly used speech features. The proposed system can predict overlapping speech with an accuracy of 84% and Fscore of 88% on a dataset of mixed speech generated based on the GRID dataset.