# Mixtures of Deep Neural Experts for Automated Speech Scoring

arXiv: https://arxiv.org/abs/2106.12475 · HTML: https://mlsanity.com/paper/2106.12475/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Hard Mixtures of Experts for Large Scale Weakly Supervised Vision](https://mlsanity.com/paper/1704.06363/) (2017)
- [TLT-school: a Corpus of Non Native Children Speech](https://mlsanity.com/paper/2001.08051/) (2020)
- [Non-native children speech recognition through transfer learning](https://mlsanity.com/paper/1809.09658/) (2018)
- [LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues](https://mlsanity.com/paper/1605.01652/) (2016)
- [Automatic assessment of spoken language proficiency of non-native children](https://mlsanity.com/paper/1903.06409/) (2019)
- [Embedding-based system for the Text part of CALL v3 shared task](https://mlsanity.com/paper/1908.02505/) (2019)

## Built on by (most cited first, 3 of 3)

- [Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact](https://mlsanity.com/paper/2507.00951/) (2025)
- [Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training](https://mlsanity.com/paper/2303.00786/) (2023)
- [Zero-Shot Grammar Competency Estimation Using Large Language Model Generated Pseudo Labels](https://mlsanity.com/paper/2511.13152/) (2025)

## Abstract (arXiv)

The paper copes with the task of automatic assessment of second language proficiency from the language learners' spoken responses to test prompts. The task has significant relevance to the field of computer assisted language learning. The approach presented in the paper relies on two separate modules: (1) an automatic speech recognition system that yields text transcripts of the spoken interactions involved, and (2) a multiple classifier system based on deep learners that ranks the transcripts into proficiency classes. Different deep neural network architectures (both feed-forward and recurrent) are specialized over diverse representations of the texts in terms of: a reference grammar, the outcome of probabilistic language models, several word embeddings, and two bag-of-word models. Combination of the individual classifiers is realized either via a probabilistic pseudo-joint model, or via a neural mixture of experts. Using the data of the third Spoken CALL Shared Task challenge, the highest values to date were obtained in terms of three popular evaluation metrics.