openclaw - 💡(How to fix) Fix [voice-call] Support directed speech and NLU/NLP IVRs (not just DTMF) [1 comments, 1 participants]

import numpy as np

class InputModeClassifier:
    def __init__(self):
        self.modes = ['dtmf_required', 'speech_directed', 'nlu_dialog', 'queue_or_hold', 'live_agent', 'unknown']
        self.mode_memory = None

    def classify(self, input_data):
        # Implement classification logic here
        # For example, using a machine learning model
        probabilities = np.array([0.1, 0.3, 0.4, 0.1, 0.05, 0.05])
        mode = np.random.choice(self.modes, p=probabilities)
        return mode

    def update_mode(self, new_mode):
        # Implement mode memory and hysteresis here
        if self.mode_memory is None:
            self.mode_memory = new_mode
        elif self.mode_memory != new_mode:
            # Require sustained evidence before switching modes
            self.mode_memory = new_mode

class ActionPolicy:
    def __init__(self):
        self.mode_actions = {
            'dtmf_required': self.dtmf_injection,
            'speech_directed': self.short_keyword_utterance,
            'nlu_dialog': self.nlu_intent_slot_filling,
            # Add actions for other modes
        }

    def take_action(self, mode, input_data):
        action = self.mode_actions.get(mode)
        if action:
            action(input_data)

    def dtmf_injection(self, input_data):
        # Implement DTMF injection logic here
        print("DTMF injection")

    def short_keyword_utterance(self, input_data):
        # Implement short keyword/intent utterance logic here
        print("Short keyword utterance")

    def nlu_intent_slot_filling(self, input_data):
        # Implement NLU intent slot-filling logic here
        print("NLU intent slot-filling")

### Verification
To verify the fix, we can use the following steps:

* Test the classifier with curated samples to ensure ≥90% correct mode classification.
* Test the action policy to ensure no unnecessary DTMF attempts in NLU-only phases.
* Test the transition to queue/live-agent detection to ensure reliability.

### Extra Tips
* Use a logging and evaluation harness to track regression and monitor performance.
* Continuously update and refine the classifier and action policy based on new data and user feedback.