# Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective

arXiv: https://arxiv.org/abs/2211.02812 · HTML: https://mlsanity.com/paper/2211.02812/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 70 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [Emergent Multi-Agent Communication in the Deep Learning Era](https://mlsanity.com/paper/2006.02419/) (2020)
- [Generate, Annotate, and Learn: NLP with Synthetic Text](https://mlsanity.com/paper/2106.06168/) (2021)
- [Disfluencies and Human Speech Transcription Errors](https://mlsanity.com/paper/1904.04398/) (2019)
- [Evolutionary forces in language change](https://mlsanity.com/paper/1608.00938/) (2016)
- [UserLibri: A Dataset for ASR Personalization Using Only Text](https://mlsanity.com/paper/2207.00706/) (2022)
- [Towards A Learning-Based Framework for Self-Driving Design of Networking Protocols](https://mlsanity.com/paper/2009.02128/) (2020)
- [MAC Protocol Design Optimization Using Deep Learning](https://mlsanity.com/paper/2002.02075/) (2020)
- [CONVINCE: Collaborative Cross-Camera Video Analytics at the Edge](https://mlsanity.com/paper/2002.03797/) (2020)
- [Communicative need modulates competition in language change](https://mlsanity.com/paper/2006.09277/) (2020)
- [Contextualized Sensorimotor Norms: multi-dimensional measures of sensorimotor strength for ambiguous English words, in context](https://mlsanity.com/paper/2203.05648/) (2022)
- [Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges](https://mlsanity.com/paper/1909.10468/) (2019)
- [On Homophony and R'enyi Entropy](https://mlsanity.com/paper/2109.13766/) (2021)
- [Unboxing MAC Protocol Design Optimization Using Deep Learning](https://mlsanity.com/paper/2002.03795/) (2020)
- [Low-Cost Traffic Sensing System Based on LoRaWAN for Urban Areas](https://mlsanity.com/paper/2211.00992/) (2022)

## Abstract (arXiv)

Automatic speech recognition (ASR) meets more informal and free-form input data as voice user interfaces and conversational agents such as the voice assistants such as Alexa, Google Home, etc., gain popularity. Conversational speech is both the most difficult and environmentally relevant sort of data for speech recognition. In this paper, we take a linguistic perspective, and take the French language as a case study toward disambiguation of the French homophones. Our contribution aims to provide more insight into human speech transcription accuracy in conditions to reproduce those of state-of-the-art ASR systems, although in a much focused situation. We investigate a case study involving the most common errors encountered in the automatic transcription of French language.