>>2934609
3. **Decision Logic**:
- Define a threshold for triggering an action (e.g., a score above 0.8 for negative tone + flagged words).
- For a proof-of-concept, log the results rather than triggering any physical response (to avoid ethical issues).
### Challenges
- **Context Sensitivity**: Sarcasm and tone are highly context-dependent. A phrase like “nice job” could be genuine or mocking.
- **False Positives**: Overly sensitive systems might flag neutral speech, while narrow systems miss subtle cues.
- **Cultural Variation**: Speech patterns vary across cultures, affecting what’s perceived as “bitchy.”
- **Real-Time Processing**: Requires low-latency transcription and analysis, which demands robust hardware.
### Example Code Snippet (Pseudo-Code)
```python
from speech_recognition import Recognizer, Microphone
import re
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2Processor
# Sample flagged words/phrases
flagged_words = ["stupid", "shut up", "obviously", "whatever", "you’re useless"]
# Initialize speech recognition
recognizer = Recognizer()
mic = Microphone()
# Initialize tone analysis model
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base")
model = Wav2Vec2ForSequenceClassification.from_pretrained("facebook/wav2vec2-base")
def analyze_speech():
with mic as source:
audio = recognizer.listen(source)
# Convert to text
text = recognizer.recognize_google(audio)
# Check for flagged words
for word in flagged_words:
if re.search(r'\b' + word + r'\b', text, re.IGNORECASE):
print(f"Flagged word detected: {word}")
# Analyze tone (simplified)
inputs = processor(audio, return_tensors="pt", sampling_rate=16000)
outputs = model(**inputs)
tone_score = outputs.logits.softmax(dim=-1) # Example: [0.1, 0.7, 0.2] for [positive, negative, neutral]
if tone_score[1] > 0.8: # Negative tone threshold
print("Negative tone detected")
