Summary of Gujarati-english Code-switching Speech Recognition Using Ensemble Prediction Of Spoken Language, by Yash Sharma et al.
Gujarati-English Code-Switching Speech Recognition using ensemble prediction of spoken language
by Yash Sharma, Basil Abraham, Preethi Jyothi
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on improving end-to-end Automatic Speech Recognition (ASR) models for code-switched speech recognition. The main challenge is recognizing the language due to similar-sounding words and accents. To address this issue, the authors propose two methods: introducing language-specific parameters in transformer layers and modifying the multi-head attention mechanism for explainability. Additionally, they implement a Temporal Loss to maintain continuity in input alignment. Although WER (Word Error Rate) is not significantly reduced, the method shows promise in predicting the correct language from spoken data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make it easier for computers to recognize what people are saying when they’re speaking two different languages at once. It’s a tricky task because some words sound similar, especially if someone has an accent. The researchers came up with new ways to make automatic speech recognition (ASR) models better. They want to help computers figure out which language is being spoken just by listening to the audio. Even though it didn’t make a huge difference in how well the computers did, this approach might be helpful for other tasks. |
Keywords
* Artificial intelligence * Alignment * Multi head attention * Transformer
