# A Novel Scheme to classify Read and Spontaneous Speech

arXiv: https://arxiv.org/abs/2306.08012 · HTML: https://mlsanity.com/paper/2306.08012/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 13 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [Automatic Speaker Independent Dysarthric Speech Intelligibility Assessment System](https://mlsanity.com/paper/2103.06157/) (2021)

## Abstract (arXiv)

The COVID-19 pandemic has led to an increased use of remote telephonic interviews, making it important to distinguish between scripted and spontaneous speech in audio recordings. In this paper, we propose a novel scheme for identifying read and spontaneous speech. Our approach uses a pre-trained DeepSpeech audio-to-alphabet recognition engine to generate a sequence of alphabets from the audio. From these alphabets, we derive features that allow us to discriminate between read and spontaneous speech. Our experimental results show that even a small set of self-explanatory features can effectively classify the two types of speech very effectively.