Summary of Human-like Linguistic Biases in Neural Speech Models: Phonetic Categorization and Phonotactic Constraints in Wav2vec2.0, by Marianne De Heer Kloots et al.
Human-like Linguistic Biases in Neural Speech Models: Phonetic Categorization and Phonotactic Constraints in Wav2Vec2.0
by Marianne de Heer Kloots, Willem Zuidema
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 Deep neural speech models, like Wav2Vec2, are designed to recognize and generate human-like speech. Researchers have previously explored how these models represent individual phonemes, or units of sound. In this study, the authors investigate how Wav2Vec2 handles interactions between phonemes, specifically how it resolves phonotactic constraints. They created a series of synthesized sounds that blend /l/ and /r/ sounds and embedded them in different linguistic contexts to test the model’s bias. The results show that Wav2Vec2 models exhibit a bias towards the most phonologically acceptable sound category, similar to human listeners. By analyzing the model’s internal representations using simple metrics, the authors found that this bias emerges early on in the Transformer module and is amplified by fine-tuning for automatic speech recognition (ASR) tasks. This study demonstrates how carefully designed stimuli can help identify specific linguistic knowledge within neural speech models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer program that can recognize and generate human-like speech. Scientists have been studying this type of program, called Wav2Vec2, to see how it works. In this research, the authors looked at how Wav2Vec2 handles different sounds when they’re mixed together. They created fake sounds that blend two specific sounds, /l/ and /r/, and put them in different sentences to test the model’s judgment. The results show that the computer program is biased towards the sound that makes more sense in a sentence, just like humans are. By looking at what’s going on inside the program, the authors found that this bias happens early on and gets stronger when the program is trained to recognize speech. |
Keywords
» Artificial intelligence » Fine tuning » Transformer
