# Improving Word Recognition in Speech Transcriptions by Decision-level Fusion of Stemming and Two-way Phoneme Pruning

arXiv: https://arxiv.org/abs/2107.12428 · HTML: https://mlsanity.com/paper/2107.12428/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild](https://mlsanity.com/paper/1810.06990/) (2018)
- [Towards Visually Grounded Sub-Word Speech Unit Discovery](https://mlsanity.com/paper/1902.08213/) (2019)
- [An investigation of phone-based subword units for end-to-end speech recognition](https://mlsanity.com/paper/2004.04290/) (2020)
- [Improving End-to-end Speech Recognition with Pronunciation-assisted Sub-word Modeling](https://mlsanity.com/paper/1811.04284/) (2018)
- [3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition](https://mlsanity.com/paper/1706.05739/) (2017)
- [Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages](https://mlsanity.com/paper/1811.04791/) (2018)
- [Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only](https://mlsanity.com/paper/1803.10952/) (2018)
- [A context sensitive real-time Spell Checker with language adaptability](https://mlsanity.com/paper/1910.11242/) (2019)
- [Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling](https://mlsanity.com/paper/1908.03538/) (2019)
- [Speaker Recognition using SincNet and X-Vector Fusion](https://mlsanity.com/paper/2004.02219/) (2020)

## Abstract (arXiv)

We introduce an unsupervised approach for correcting highly imperfect speech transcriptions based on a decision-level fusion of stemming and two-way phoneme pruning. Transcripts are acquired from videos by extracting audio using Ffmpeg framework and further converting audio to text transcript using Google API. In the benchmark LRW dataset, there are 500 word categories, and 50 videos per class in mp4 format. All videos consist of 29 frames (each 1.16 s long) and the word appears in the middle of the video. In our approach we tried to improve the baseline accuracy from 9.34% by using stemming, phoneme extraction, filtering and pruning. After applying the stemming algorithm to the text transcript and evaluating the results, we achieved 23.34% accuracy in word recognition. To convert words to phonemes we used the Carnegie Mellon University (CMU) pronouncing dictionary that provides a phonetic mapping of English words to their pronunciations. A two-way phoneme pruning is proposed that comprises of the two non-sequential steps: 1) filtering and pruning the phonemes containing vowels and plosives 2) filtering and pruning the phonemes containing vowels and fricatives. After obtaining results of stemming and two-way phoneme pruning, we applied decision-level fusion and that led to an improvement of word recognition rate upto 32.96%.