# Using Deep Learning Techniques and Inferential Speech Statistics for AI Synthesised Speech Recognition

arXiv: https://arxiv.org/abs/2107.11412 · HTML: https://mlsanity.com/paper/2107.11412/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 23 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://mlsanity.com/paper/1603.04351/) (2016)
- [Detection of AI-Synthesized Speech Using Cepstral & Bispectral Statistics](https://mlsanity.com/paper/2009.01934/) (2020)
- [Multi-task WaveNet: A Multi-task Generative Model for Statistical Parametric Speech Synthesis without Fundamental Frequency Conditions](https://mlsanity.com/paper/1806.08619/) (2018)

## Built on by (most cited first, 1 of 1)

- [Detection of AI Synthesized Hindi Speech](https://mlsanity.com/paper/2203.03706/) (2022)

## Abstract (arXiv)

The recent developments in technology have re-warded us with amazing audio synthesis models like TACOTRON and WAVENETS. On the other side, it poses greater threats such as speech clones and deep fakes, that may go undetected. To tackle these alarming situations, there is an urgent need to propose models that can help discriminate a synthesized speech from an actual human speech and also identify the source of such a synthesis. Here, we propose a model based on Convolutional Neural Network (CNN) and Bidirectional Recurrent Neural Network (BiRNN) that helps to achieve both the aforementioned objectives. The temporal dependencies present in AI synthesized speech are exploited using Bidirectional RNN and CNN. The model outperforms the state-of-the-art approaches by classifying the AI synthesized audio from real human speech with an error rate of 1.9% and detecting the underlying architecture with an accuracy of 97%.