Multimodal Fusion for Speaker Diarization

1# Multimodal Fusion for Speaker Diarization
2 
3## Overview
4 
5When working with video files, you can significantly improve speaker diarization by combining audio features with visual features like face detection and lip movement analysis.
6 
7## When to Use
8 
9- Processing video files (not just audio)
10- Multiple speakers visible on screen
11- Need to disambiguate speakers with similar voices
12- Improve accuracy by leveraging visual cues
13 
14## Visual Feature Extraction
15 
16### Face Detection
17 
18```python
19import cv2
20import numpy as np
21 
22# Initialize face detector
23face_cascade = cv2.CascadeClassifier(
24    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
25)
26 
27# Process video frames
28cap = cv2.VideoCapture(video_path)
29fps = cap.get(cv2.CAP_PROP_FPS)
30faces_by_time = {}
31 
32frame_count = 0
33frame_skip = max(1, int(fps / 2))  # Process every other frame
34 
35while cap.isOpened():
36    ret, frame = cap.read()
37    if not ret:
38        break
39 
40    if frame_count % frame_skip == 0:
41        timestamp = frame_count / fps
42        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
43        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
44        faces_by_time[timestamp] = len(faces)
45 
46    frame_count += 1
47 
48cap.release()
49```
50 
51### Lip Movement Detection
52 
53```python
54lip_movement_by_time = {}
55prev_mouth_roi = None
56 
57while cap.isOpened():
58    ret, frame = cap.read()
59    if not ret:
60        break
61 
62    if frame_count % frame_skip == 0:
63        timestamp = frame_count / fps
64…