# Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency

arXiv: https://arxiv.org/abs/2111.15156 · HTML: https://mlsanity.com/paper/2111.15156/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 67 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 15 of 15)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure](https://mlsanity.com/paper/2101.00387/) (2021)
- [Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach](https://mlsanity.com/paper/2006.04628/) (2020)
- [Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments](https://mlsanity.com/paper/1705.10874/) (2017)
- [A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification](https://mlsanity.com/paper/1810.05754/) (2018)
- [SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring](https://mlsanity.com/paper/1711.04981/) (2017)
- [Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers](https://mlsanity.com/paper/2012.11243/) (2020)
- [Speaker-Conditioned Hierarchical Modeling for Automated Speech Scoring](https://mlsanity.com/paper/2109.00928/) (2021)
- [audino: A Modern Annotation Tool for Audio and Speech](https://mlsanity.com/paper/2006.05236/) (2020)
- [My Teacher Thinks The World Is Flat! Interpreting Automatic Essay Scoring Mechanism](https://mlsanity.com/paper/2012.13872/) (2020)
- [Multi-modal Automated Speech Scoring using Attention Fusion](https://mlsanity.com/paper/2005.08182/) (2020)
- [Towards Modelling Coherence in Spoken Discourse](https://mlsanity.com/paper/2101.00056/) (2020)
- [Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees](https://mlsanity.com/paper/2111.08906/) (2021)

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

- [Span Classification with Structured Information for Disfluency Detection in Spoken Utterances](https://mlsanity.com/paper/2203.16028/) (2022)
- [H-AES: Towards Automated Essay Scoring for Hindi](https://mlsanity.com/paper/2302.14635/) (2023)
- [Addressing Cold Start Problem for End-to-end Automatic Speech Scoring](https://mlsanity.com/paper/2306.14310/) (2023)
- [Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation](https://mlsanity.com/paper/2605.29430/) (2026)
- [Automated evaluation of children's speech fluency for low-resource languages](https://mlsanity.com/paper/2505.19671/) (2025)
- [Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers](https://mlsanity.com/paper/2607.16085/) (2026)

## Abstract (arXiv)

English proficiency assessments have become a necessary metric for filtering and selecting prospective candidates for both academia and industry. With the rise in demand for such assessments, it has become increasingly necessary to have the automated human-interpretable results to prevent inconsistencies and ensure meaningful feedback to the second language learners. Feature-based classical approaches have been more interpretable in understanding what the scoring model learns. Therefore, in this work, we utilize classical machine learning models to formulate a speech scoring task as both a classification and a regression problem, followed by a thorough study to interpret and study the relation between the linguistic cues and the English proficiency level of the speaker. First, we extract linguist features under five categories (fluency, pronunciation, content, grammar and vocabulary, and acoustic) and train models to grade responses. In comparison, we find that the regression-based models perform equivalent to or better than the classification approach. Second, we perform ablation studies to understand the impact of each of the feature and feature categories on the performance of proficiency grading. Further, to understand individual feature contributions, we present the importance of top features on the best performing algorithm for the grading task. Third, we make use of Partial Dependence Plots and Shapley values to explore feature importance and conclude that the best performing trained model learns the underlying rubrics used for grading the dataset used in this study.