Building an AI Accent Detector: How We Analyze Voice to Identify Regional Origins
Building an AI Accent Detector: How We Analyze Voice to Identify Regional Origins
Can you really tell where someone is from just by how they speak? Turns out, AI can—if you feed it the right signals.
In this article, we’ll go deep into how I built an AI system that listens to your voice and predicts your regional accent. This post focuses on the technical pipeline, including audio preprocessing, feature extraction, and model design.
Whether you're building something similar or just curious about how this works, you'll find a complete breakdown here.
🎯 Goal of the System
To keep things clear: this is not speaker identification or speech-to-text.
Objective: Given a short audio recording of a person speaking English, predict their accent or region of origin (e.g., US Midwest, Indian English, Australian English, etc.).
🧱 Architecture Overview
Here’s a high-level view of the pipeline:
🎤 Audio Input → 🔊 Audio Preprocessing → 🎼 Feature Extraction (MFCCs) → 🧠 Accent Classifier (CNN/Transformer) → 📊 Region Prediction Output
🎤 Step 1: Audio Input & Preprocessing
We use simple tools like pyaudio or browser microphone input to capture short samples, typically 3–6 seconds.
Before feeding audio to the model, we:
- Convert to mono (1 channel)
- Resample to 16 kHz
- Trim silence using energy threshold
- Normalize amplitude between -1.0 and 1.0
We use librosa for most of this:
import librosa
y, sr = librosa.load("voice_sample.wav", sr=16000)
y_trimmed, _ = librosa.effects.trim(y)
🎼 Step 2: Feature Extraction with MFCCs
Accent lies in how we speak—not what we say. So we don’t need transcription.
Instead, we extract Mel-Frequency Cepstral Coefficients (MFCCs), which capture vocal timbre and prosody.
mfcc = librosa.feature.mfcc(y=y_trimmed, sr=16000, n_mfcc=13)
We also add delta and delta-delta features to capture changes over time:
import librosa
mfccs = librosa.feature.mfcc(y, sr=16000, n_mfcc=13)
delta = librosa.feature.delta(mfccs)
delta2 = librosa.feature.delta(mfccs, order=2)
features = np.vstack([mfccs, delta, delta2]) # Shape: (39, time_steps)
🧠 Step 3: Model Design
We experimented with two main architectures:
- CNN (Convolutional Neural Network) This model treats the MFCC array like an image (channels x time). We use 2D convolutions to capture local patterns in speech.
Pros:
-
Easy to train
-
Works well with short samples
- Transformer (Self-Attention) Inspired by models like Wav2Vec and Whisper, we added attention-based encoders to learn long-term temporal dependencies.
Pros:
-
Better context understanding
-
More accurate on multilingual data
🏷️ Step 4: Accent Labels & Dataset
We trained the model on a mix of open datasets:
- Common Voice
- AccentDB
- Custom user submissions (with consent)
Labeling includes coarse regions like:
- North American
- British Isles
- Indian Subcontinent
- Southeast Asia
- Africa
- Australia / NZ
You can further fine-tune into sub-regions if your dataset supports it.
🧪 Evaluation & Accuracy
We used stratified cross-validation across speakers to avoid overfitting.
- Accuracy (Top-1): ~79% on 6-region classifier
- Top-2 Accuracy: ~91%
- Inference latency: < 200ms on CPU
Confusion matrices showed occasional overlaps between Indian and Southeast Asian accents, and between UK regional accents.
🔐 Privacy & Deployment Notes
Voice is personal. We made sure:
- No raw audio is stored
- All processing is ephemeral or on-device
- Models run in lightweight containers (e.g., TensorFlow Lite or WebAssembly for browser)
🔚 Conclusion
Accent recognition is a fascinating intersection of linguistics and machine learning. While challenging, it offers a new way to explore human diversity through AI.
Whether you're building a global assistant, analyzing dialect drift, or just having fun with tech—accent AI opens a world of possibilities.