Show HN: I trained a 9M speech model to fix my Mandarin tones (simedw.com)

Article summary

The author trained a 9M-parameter speech model to help improve their Mandarin pronunciation, particularly with tones. The model uses a Conformer encoder with CTC loss and can run entirely on-device. The author found that the model was effective in identifying pronunciation errors and providing feedback. The model is available for others to try through a live demo.

Main themes

• Mandarin pronunciation
• Speech recognition
• Tone learning
• Language learning
• Deep learning
• Pronunciation feedback

What commenters say

• The tool is useful for language learners, but may not accurately handle tone transformations and regional accents.
• Over-exaggerating tones while learning can help cement the correct pronunciation, and then the learner can tone it down to sound more natural.
• The model's effectiveness is limited by its training data, and adding more conversational datasets could improve its performance.
• Native speakers may not be accurately assessed by the model due to variations in pronunciation and speaking style.
• The tool has potential, but needs to address issues with longer sentences and faster speech to be more useful for intermediate learners.
• Tone recognition is a challenging aspect of learning a tonal language, and even native speakers of other languages may struggle with it.
• The model's ability to provide feedback on pronunciation is valuable, but its accuracy and limitations need to be considered.
• The tool could be improved by incorporating more features, such as handling tone sandhi and regional accents, to make it more effective for learners.