writeup/writeup.md

LinguaWave — Competition Writeup

Task: Language Identification — 8-class audio classification
Metric: Macro F1-score
Dataset: 8,000 train samples, 4,000 test samples (1,000 / 500 per language)
Source: Google FLEURS (CC BY 4.0)
Languages: Indonesian (id), Malay (ms), Vietnamese (vi), Thai (th), English (en), Chinese (zh), Arabic (ar), French (fr)

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Approach Progression

#	Approach	Val Macro F1	Description
1	MFCC + SVM	0.9775	40-dim MFCC mean+std → SVM RBF, C=10
2	Pitch + LightGBM	0.9594	~80-dim acoustic + prosody → LGBM
3	Bag of Codewords	0.6290	k-means codebook (k=64) + histogram → LogReg
4	CNN on Log-Mel	0.8960	128×625 log-mel → 4-block CNN, BATCH=64, 5 epochs
5	Multi-Scale CNN	0.9682	3-branch CNN (n_fft=512/1024/2048) + hard-neg mining on id/ms, precomputed mel cache

Key Takeaways

• Language ID is surprisingly easy with MFCCs: at 0.9775, approach 1 already nears the ceiling. The 8 languages span distinct phoneme inventories, rhythm, and prosody, making them linearly separable in MFCC space.
• More features ≠ better (approach 2 < 1): adding pitch and prosody (F0, jitter, shimmer, HNR) slightly hurts because these features are noisier over 10-second clips with variable speech content. The MFCC SVM is more robust.
• Codebook approach underperforms (0.63): bag-of-codewords with k=64 compresses too aggressively. Language ID benefits from temporal dynamics (captured by delta-MFCC in approach 1) that flat histograms discard.
• Hard negative mining for id/ms: Indonesian and Malay share ~60% vocabulary and acoustic similarity. The multi-scale CNN addresses this by oversampling these pairs during training (2× factor).
• Multi-scale spectrograms: using n_fft=512/1024/2048 captures short-term phonemes (512), syllable-level rhythm (1024), and prosodic contours (2048) simultaneously. However, the multi-branch architecture converges unstably (F1 oscillating 0.30–0.78) — a lower learning rate and warm-up schedule are recommended.

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Dataset Details

• All clips from FLEURS dataset, filtered to 3–12 seconds duration
• Padded/trimmed to exactly 10 seconds (160,000 samples at 16 kHz)
• Stratified 80/20 split: 1,000 train / 500 test per language
• Class balance is exact — macro F1 = accuracy in this case

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Tips for Participants

1. Approach 1 sets a very high bar: if your CNN underperforms the MFCC SVM (0.9775), your model is not learning — check preprocessing and batch size.
2. Memory-aware mel computation: the mel shape is (1, 128, 625) at 16 kHz / 10s. With BATCH=64 this is ~64 MB/batch — safe on Colab T4. Do NOT use batch >128 without checking memory.
3. id vs ms is the hard pair: confusion between Indonesian and Malay is the main error source. Consider adding language pair augmentation or a dedicated hard-negative loss.
4. Cache your mel spectrograms: approach 5 precomputes all mels to numpy arrays before training. This cuts training time from 27 hours (on-the-fly) to ~30 minutes.
5. LabelEncoder order matters: ensure consistent label encoding between train/val/test splits. Always fit the encoder on training data only.
6. For approach 3 improvement: try k=256 codebook, Fisher vectors instead of BoW histograms, and SVM classifier — this should recover 10–15 points vs. k=64 + LogReg.

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Hardest Language Pairs (id/ms)

Indonesian and Malay are the most commonly confused pair. Strategies that help:

• Train on more diverse speakers per language
• Use character n-gram language models on ASR transcripts
• Temporal pooling over the full 10-second clip rather than just mean/std

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Reproducibility

All scripts use SEED = 42. Feature extraction caches are keyed by script number (e.g., lw_01_train.npy). The CNN uses torch.manual_seed(42). joblib parallel feature extraction is thread-based (prefer="threads") for librosa compatibility.

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Baseline Comparison

Method	Val Macro F1	Notes
Majority class	0.125	8 balanced classes
Approach 3 (BoW k=64)	0.6290	Histogram loses temporal dynamics
Approach 4 (CNN Log-Mel)	0.8960	5 epochs; converged well at epoch 5
Approach 5 (MultiScale CNN)	0.9682	Best epoch 5; precomputed mel cache
Approach 2 (Pitch LGBM)	0.9594	Prosody features + gradient boosting
Approach 1 (MFCC SVM)	0.9775	Best overall — classical ML wins here
Human (expert)	~0.98	Native speakers, clean recordings