Social Voice Networks
“Building recommendation and moderation systems for voice-based social platforms.”
1. What are Social Voice Networks?
Social Voice Networks are platforms where users interact primarily through live audio rather than text or images.
Examples:
- Clubhouse: Live audio rooms with speakers and listeners.
- Twitter Spaces: Audio conversations linked to Twitter.
- Discord Voice Channels: Real-time voice chat for gaming/communities.
- LinkedIn Audio: Professional networking via voice events.
Unique Challenges:
- Ephemeral: Content disappears (unlike text posts).
- Real-time Moderation: Can’t wait for human review.
- Speaker Identification: Who said what?
- Content Recommendation: Suggest relevant rooms/conversations.
2. System Architecture
┌──────────────────────────────────────────────┐
│ Social Voice Network Platform │
├──────────────────────────────────────────────┤
│ │
│ ┌────────────┐ ┌────────────┐ │
│ │ Live Audio │ │ Speaker │ │
│ │ Streams │ │Recognition │ │
│ └──────┬─────┘ └──────┬─────┘ │
│ │ │ │
│ v v │
│ ┌──────────────────────────────┐ │
│ │ ASR (Speech-to-Text) │ │
│ └──────────┬───────────────────┘ │
│ │ │
│ v │
│ ┌──────────────────────────────┐ │
│ │ Content Moderation │ │
│ │ (Toxicity, Misinformation) │ │
│ └──────────┬───────────────────┘ │
│ │ │
│ v │
│ ┌──────────────────────────────┐ │
│ │ Topic Extraction & Indexing │ │
│ └──────────┬───────────────────┘ │
│ │ │
│ v │
│ ┌──────────────────────────────┐ │
│ │ Recommendation Engine │ │
│ │ (User → Room matching) │ │
│ └──────────────────────────────┘ │
│ │
└──────────────────────────────────────────────┘
3. Speaker Recognition (Diarization)
Problem: In a room with 10 speakers, attribute each utterance to the correct speaker.
x-Vector Embeddings
Architecture:
Audio (MFCC features)
↓
TDNN (Time Delay Neural Network)
↓
Statistics Pooling (mean + std over time)
↓
Fully Connected Layers
↓
x-vector (512-dim embedding)
Training: Softmax loss over speaker IDs. Inference: Extract x-vector for each segment, cluster to identify speakers.
import torch
import torchaudio
from speechbrain.pretrained import EncoderClassifier
# Load pre-trained x-vector model
classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-xvect-voxceleb")
def extract_speaker_embedding(audio_path):
# Load audio
signal, fs = torchaudio.load(audio_path)
# Extract x-vector
embeddings = classifier.encode_batch(signal)
return embeddings.squeeze() # [512]
# Clustering speakers
from sklearn.cluster import AgglomerativeClustering
embeddings = [extract_speaker_embedding(segment) for segment in segments]
clustering = AgglomerativeClustering(n_clusters=None, distance_threshold=0.5)
labels = clustering.fit_predict(embeddings)
# labels[i] = speaker ID for segment i
Speaker Change Detection
Problem: Detect when a new speaker starts talking.
Approach: Bayesian Information Criterion (BIC)
For each potential change point t:
Model 1: [0, t] and [t+1, T] as two separate Gaussians
Model 2: [0, T] as one Gaussian
ΔBIC = BIC(Model1) - BIC(Model2)
If ΔBIC > threshold: Change point detected
Neural Approach: LSTM that predicts change points.
class SpeakerChangeDetector(nn.Module):
def __init__(self):
self.lstm = nn.LSTM(input_size=40, hidden_size=256, num_layers=2, bidirectional=True)
self.fc = nn.Linear(512, 2) # Binary classification: change or no change
def forward(self, mfcc):
# mfcc: [batch, time, 40]
lstm_out, _ = self.lstm(mfcc)
logits = self.fc(lstm_out) # [batch, time, 2]
return logits
4. Real-time ASR for Transcription
Challenge: Transcribe live audio with < 200ms latency.
Solution: Streaming RNN-T (RNN-Transducer)
Architecture:
- Encoder: Processes audio chunks (e.g., 80ms frames).
- Prediction Network: Language model over previously emitted tokens.
- Joint Network: Combines encoder + prediction to emit tokens.
class StreamingRNNT(nn.Module):
def __init__(self):
self.encoder = ConformerEncoder(streaming=True)
self.prediction = nn.LSTM(input_size=vocab_size, hidden_size=512)
self.joint = nn.Linear(512 + 512, vocab_size)
def forward(self, audio_chunk, prev_token, prev_hidden):
# Encode audio
enc_out = self.encoder(audio_chunk) # [1, 512]
# Predict next token
pred_out, hidden = self.prediction(prev_token, prev_hidden) # [1, 512]
# Joint
joint_out = self.joint(torch.cat([enc_out, pred_out], dim=1)) # [1, vocab_size]
# Greedy decode
token = joint_out.argmax(dim=1)
return token, hidden
Latency Breakdown:
- Audio chunk: 80ms
- Encoder: 50ms
- Prediction + Joint: 10ms
- Total: ~140ms (meets real-time requirement)
5. Content Moderation
Challenge: Detect toxic speech, misinformation, harassment in real-time.
Toxicity Detection
Pipeline:
- ASR: Audio → Text.
- Text Classifier: BERT-based toxicity detector.
- Audio Features: Prosody (shouting, aggressive tone).
- Fusion: Combine text + audio scores.
class ToxicityDetector(nn.Module):
def __init__(self):
self.text_encoder = BERTModel()
self.audio_encoder = ResNet1D() # Conv1D on mel-spectrogram
self.fusion = nn.Linear(768 + 256, 2) # Binary: toxic or not
def forward(self, text_tokens, audio):
text_emb = self.text_encoder(text_tokens).pooler_output # [batch, 768]
audio_emb = self.audio_encoder(audio).squeeze() # [batch, 256]
combined = torch.cat([text_emb, audio_emb], dim=1)
logits = self.fusion(combined)
return logits
# Inference
text = asr(audio_chunk)
logits = toxicity_detector(text, audio_chunk)
is_toxic = logits.argmax(dim=1) == 1
if is_toxic:
# Mute speaker, alert moderators
send_alert(speaker_id, timestamp)
Misinformation Detection
Challenge: “This vaccine contains microchips” needs to be flagged.
Approach:
- Fact-Checking API: Query external fact-checkers (Snopes, FactCheck.org).
- Claim Detection: NER to extract claims (“vaccine contains microchips”).
- Verification: Compare claim against knowledge base.
def detect_misinformation(transcript):
# Extract claims using NER
claims = ner_model.extract_claims(transcript)
for claim in claims:
# Query fact-checking APIs
fact_check_result = fact_check_api.verify(claim)
if fact_check_result.confidence > 0.8 and fact_check_result.verdict == "false":
return True, claim
return False, None
6. Topic Extraction and Tagging
Problem: Tag each room with topics (e.g., “Technology”, “Startup Funding”, “AI”).
Approach: LDA + Neural Topic Models
Latent Dirichlet Allocation (LDA)
from sklearn.decomposition import LatentDirichletAllocation
# Collect transcripts from a room
transcripts = [asr(audio) for audio in room_audio_chunks]
combined_text = " ".join(transcripts)
# Vectorize
vectorizer = CountVectorizer(max_features=1000)
X = vectorizer.fit_transform([combined_text])
# LDA
lda = LatentDirichletAllocation(n_components=10)
topics = lda.fit_transform(X)
# Top topic
top_topic_id = topics.argmax()
Neural Topic Model (with BERT)
class NeuralTopicModel(nn.Module):
def __init__(self, num_topics=100):
self.encoder = BERTModel()
self.topic_layer = nn.Linear(768, num_topics)
def forward(self, text):
emb = self.encoder(text).pooler_output # [batch, 768]
topic_dist = F.softmax(self.topic_layer(emb), dim=1) # [batch, num_topics]
return topic_dist
# Tag room
topic_dist = neural_topic_model(room_transcript)
top_topics = topic_dist.argsort(descending=True)[:3] # Top 3 topics
room_tags = [topic_names[t] for t in top_topics]
7. Room Recommendation (User → Room Matching)
Challenge: Suggest relevant rooms to users.
Graph-based Approach
Graph:
- Nodes: Users, Rooms, Topics.
- Edges:
- User –joined–> Room
- Room –tagged_with–> Topic
- User –interested_in–> Topic
Recommendation:
- Random Walk: Start from user, walk through graph.
- Frequency: Rooms visited most often in walks are recommended.
def personalized_pagerank(graph, user_id, alpha=0.85, num_walks=1000):
scores = defaultdict(float)
for _ in range(num_walks):
current = user_id
for step in range(10):
if random.random() < (1 - alpha):
current = user_id # Restart
else:
neighbors = graph.neighbors(current)
if neighbors:
current = random.choice(neighbors)
if graph.node_type(current) == "Room":
scores[current] += 1
return sorted(scores.items(), key=lambda x: x[1], reverse=True)[:10]
Collaborative Filtering
Matrix: Users × Rooms (1 if user joined room, 0 otherwise). Matrix Factorization: \[ R \approx U V^T \] where \(U\) is user embeddings, \(V\) is room embeddings.
class MatrixFactorization(nn.Module):
def __init__(self, num_users, num_rooms, k=128):
self.user_emb = nn.Embedding(num_users, k)
self.room_emb = nn.Embedding(num_rooms, k)
def forward(self, user_id, room_id):
u = self.user_emb(user_id) # [batch, k]
v = self.room_emb(room_id) # [batch, k]
return (u * v).sum(dim=1) # Dot product
# Training
loss = mse_loss(model(user_batch, room_batch), labels)
Content-based Filtering
Idea: Recommend rooms similar to those the user joined before.
# Extract room content features
room_features = {
room_id: topic_model(room_transcript) for room_id in rooms
}
# User profile: average of joined rooms
user_profile = np.mean([room_features[r] for r in user_joined_rooms], axis=0)
# Recommend by cosine similarity
recommendations = sorted(
[(r, cosine_similarity(user_profile, room_features[r])) for r in rooms],
key=lambda x: x[1],
reverse=True
)[:10]
Deep Dive: Clubhouse’s Recommendation Algorithm
Clubhouse uses a multi-stage funnel:
Stage 1: Candidate Generation
- Social Graph: Rooms that user’s friends are in (95% of recommendations come from social graph).
- Topic Graph: Rooms tagged with user’s interested topics.
- Collaborative Filtering: “Users similar to you joined these rooms.”
Stage 2: Ranking
Features:
- User Features: Interests, past room joins, time of day.
- Room Features: Number of speakers, current topic, speaker reputation.
- Interaction Features: Number of friends in the room, historical engagement.
Model: Gradient Boosted Trees (XGBoost). Target: P(user joins and stays > 5 minutes).
Stage 3: Diversity
Re-rank to ensure variety (not all tech rooms, not all from same friend).
Maximal Marginal Relevance (MMR): \[ \text{Score}(r) = \lambda \cdot \text{Relevance}(r) - (1 - \lambda) \cdot \max_{r’ \in S} \text{Similarity}(r, r’) \] where \(S\) is the set of already selected rooms.
Deep Dive: Discord’s Voice Activity Detection (VAD)
Challenge: Detect when a user is speaking vs. background noise.
Traditional VAD: Energy threshold (volume > X dB). Problem: Fails with background noise (TV, music).
Neural VAD:
class NeuralVAD(nn.Module):
def __init__(self):
self.lstm = nn.LSTM(input_size=40, hidden_size=128, num_layers=2)
self.fc = nn.Linear(128, 2) # Speech or silence
def forward(self, mfcc):
# mfcc: [batch, time, 40]
lstm_out, _ = self.lstm(mfcc)
logits = self.fc(lstm_out[:, -1, :]) # Use last timestep
return logits
# Inference
is_speaking = neural_vad(audio_chunk).argmax(dim=1) == 1
if is_speaking:
transmit_audio_to_server()
Benefit: Reduces bandwidth by 80% (don’t transmit silence).
Deep Dive: Echo Cancellation for Voice Chat
Problem: User A hears User B. User B hears their own voice echoed back (loop).
Acoustic Echo Cancellation (AEC):
Reference signal: What we played (User B's voice)
Microphone signal: What we recorded (User A speaking + User B's echo)
Goal: Subtract the echo from the microphone signal
Adaptive Filter (NLMS - Normalized Least Mean Squares):
def nlms_aec(reference, microphone, step_size=0.01, filter_length=512):
h = np.zeros(filter_length) # Adaptive filter coefficients
output = []
for n in range(filter_length, len(microphone)):
# Reference window
x = reference[n - filter_length:n]
# Predicted echo
echo_estimate = np.dot(h, x)
# Error (remove echo)
error = microphone[n] - echo_estimate
output.append(error)
# Update filter (NLMS)
h += (step_size / (np.dot(x, x) + 1e-8)) * error * x
return np.array(output)
Modern Approach: End-to-end neural AEC (Facebook’s Demucs).
Deep Dive: Noise Suppression (Krisp, NVIDIA RTX Voice)
Problem: Background noise (dogs barking, keyboard clicks, traffic).
Solution: Deep Learning Noise Suppression
Architecture: U-Net on Spectrogram
class NoiseSuppressionUNet(nn.Module):
def __init__(self):
self.encoder = nn.Sequential(
nn.Conv2d(1, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2)
)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2),
nn.ReLU(),
nn.ConvTranspose2d(64, 1, kernel_size=2, stride=2),
nn.Sigmoid() # Mask (0 to 1)
)
def forward(self, noisy_spec):
# noisy_spec: [batch, 1, freq, time]
enc = self.encoder(noisy_spec)
mask = self.decoder(enc) # [batch, 1, freq, time]
clean_spec = noisy_spec * mask # Element-wise multiply
return clean_spec
Training:
- Input: Noisy spectrogram.
- Target: Clean spectrogram.
- Loss: L1 loss on magnitude spectrogram.
Inference: 10-20ms latency (real-time capable).
Deep Dive: Speaker Identification for Personalization
Problem: Recognize specific users by their voice (not just cluster speakers).
Approach: Speaker Verification
class SpeakerVerifier(nn.Module):
def __init__(self):
self.encoder = ResNet1D() # Extract speaker embedding
def forward(self, audio_enrollment, audio_test):
emb_enroll = self.encoder(audio_enrollment) # [1, 512]
emb_test = self.encoder(audio_test) # [1, 512]
# Cosine similarity
similarity = F.cosine_similarity(emb_enroll, emb_test)
return similarity # > 0.8 → Same speaker
# Enrollment
user_emb = model.encoder(user_audio_samples)
db.store(user_id, user_emb)
# Test
test_emb = model.encoder(test_audio)
similarity = cosine_similarity(test_emb, db.get(user_id))
if similarity > 0.8:
authenticated = True
Use Case: Automatically mute/unmute the correct participant in a meeting.
Deep Dive: Bandwidth Optimization (Opus Codec)
Challenge: Stream high-quality audio with minimal bandwidth.
Opus Codec:
- Bitrate: 6-510 kbps (adaptive).
- Latency: 5-66
ms.
- Quality: Superior to MP3 at same bitrate.
Adaptive Bitrate:
def adjust_bitrate(network_conditions):
if network_conditions['bandwidth'] > 100: # kbps
return 128 # High quality
elif network_conditions['bandwidth'] > 50:
return 64 # Medium quality
else:
return 24 # Low quality (voice still intelligible)
Deep Dive: Scalability (Handling Million Concurrent Users)
Architecture:
┌──────────────┐
│ Load Balancer│
└───────┬───────┘
│
┌────────────┼────────────┐
│ │ │
┌───▼───┐ ┌────▼────┐ ┌────▼────┐
│Server1│ │ Server2 │ │ Server3 │
└───────┘ └─────────┘ └─────────┘
│ │ │
└────────────┼────────────┘
│
┌───────▼────────┐
│ Media Server │
│ (Janus, Jitsi)│
└────────────────┘
Techniques:
- WebRTC SFU (Selective Forwarding Unit): Server forwards audio streams without decoding/encoding (low CPU).
- Regional Servers: Route users to nearest server (reduce latency).
- Adaptive Quality: Reduce bitrate under load.
Clubhouse Scale:
- Peak: 2M concurrent users.
- Solution: Agora.io (infrastructure provider) with auto-scaling.
Implementation: Full Social Voice Network Backend
import torch
import torch.nn as nn
from transformers import BertModel
import torchaudio
class SocialVoiceBackend:
def __init__(self):
self.asr_model = load_asr_model()
self.speaker_recognition = load_xvector_model()
self.toxicity_detector = ToxicityDetector()
self.topic_model = NeuralTopicModel()
self.recommender = GraphRecommender()
def process_audio_chunk(self, audio_chunk, room_id, user_id):
# 1. Speaker Recognition
speaker_emb = self.speaker_recognition.encode(audio_chunk)
speaker_id = self.cluster_speaker(speaker_emb, room_id)
# 2. ASR
transcript = self.asr_model.transcribe(audio_chunk)
# 3. Content Moderation
is_toxic, reason = self.toxicity_detector(transcript, audio_chunk)
if is_toxic:
self.mute_speaker(speaker_id, room_id, reason)
return
# 4. Update room metadata
self.update_room_transcript(room_id, transcript)
# 5. Extract topics (every 60 seconds)
if should_update_topics(room_id):
topics = self.topic_model(get_room_transcript(room_id))
self.update_room_topics(room_id, topics)
def recommend_rooms(self, user_id, top_k=10):
# Get user interests
user_profile = self.get_user_profile(user_id)
# Candidate generation
candidates = []
# 1. Social graph
friends = self.get_friends(user_id)
candidates += self.get_rooms_with_users(friends)
# 2. Topic matching
candidates += self.get_rooms_by_topics(user_profile['interests'])
# 3. Collaborative filtering
similar_users = self.find_similar_users(user_id)
candidates += self.get_popular_rooms_among(similar_users)
# Rank candidates
scores = self.recommender.rank(user_id, candidates)
# Diversify
diverse_rooms = self.apply_mmr(scores, lambda_param=0.7)
return diverse_rooms[:top_k]
# Usage
backend = SocialVoiceBackend()
# Process incoming audio
for audio_chunk in stream:
backend.process_audio_chunk(audio_chunk, room_id="tech_talk_123", user_id="alice")
# Recommend rooms
recommendations = backend.recommend_rooms(user_id="alice", top_k=10)
Top Interview Questions
Q1: How do you handle speaker overlap (two people speaking simultaneously)? Answer: Use source separation models (e.g., Conv-TasNet, Sudo RM-RF) to separate the overlapping voices into individual tracks. Then run ASR and speaker recognition on each track separately.
Q2: How do you ensure low latency for global users? Answer:
- Deploy servers in multiple regions (US East, US West, Europe, Asia).
- Route users to nearest server using GeoDNS.
- Use CDN for static assets.
- Optimize codec (Opus) with adaptive bitrate.
Q3: How do you detect and prevent spam/abuse in voice rooms? Answer:
- Real-time ASR + Toxicity Detection: Flag toxic speech immediately.
- Rate Limiting: Limit number of rooms a user can create per day.
- Reputation System: Users with low reputation (many reports) are auto-moderated.
- Audio Fingerprinting: Detect and block pre-recorded spam ads.
Q4: How do you make recommendations work for new users (cold start)? Answer:
- Onboarding: Ask users to select interests during signup.
- Popular Rooms: Show trending rooms to new users.
- Social Graph: If user connects social accounts, bootstrap recommendations from friends’ activity.
Key Takeaways
- Real-time Constraints: ASR, speaker recognition, moderation must run in < 200ms.
- Speaker Diarization: x-vector embeddings + clustering to attribute speech.
- Content Moderation: Combine text (ASR output) + audio (prosody) for toxicity detection.
- Recommendations: Graph-based (social graph + topic graph) outperform pure collaborative filtering.
- Scalability: Use SFU architecture, regional servers, adaptive bitrate for millions of concurrent users.
Summary
| Aspect | Insight |
|---|---|
| Core Components | ASR, Speaker Recognition, Moderation, Recommendation |
| Key Challenges | Real-time latency, ephemeral content, cold start |
| Architectures | Streaming RNN-T (ASR), x-vector (Speaker), GNN (Recommendations) |
| Real-World | Clubhouse, Discord, Twitter Spaces |
Originally published at: arunbaby.com/speech-tech/0030-social-voice-networks
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