Progress-Aware Text-to-Speech for Natural Barge-In Responses
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Solution Overview
Problem
Digital assistants often respond unnaturally when users interrupt their speech, requiring additional clarifications that disrupt the conversation flow.
Innovation Solution
A progress-aware digital assistant determines the playback status of output speech and identifies a subset of terms before a user's barge-in utterance, allowing it to generate a contextually relevant response without additional user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the digital assistant continues speaking without interruption, then the conversation flow is maintained, but the user cannot provide timely feedback or corrections
Solution Approach 1:
The system dynamically adjusts its behavior based on real-time conditions. When a user barge-in utterance is detected during TTS playback, the system pauses the current speech output and processes the user's interruption, then resumes the conversation flow with an updated response. This dynamic adaptation allows the assistant to respond to user feedback while maintaining overall conversation efficiency.
Solution Approach 2:
The system implements feedback by detecting user barge-in utterances during TTS playback and using this information to modify the conversation flow. The user's interruption serves as feedback that triggers a pause in speech output and generates an updated response, ensuring the assistant responds appropriately to user input while maintaining natural conversation flow.
2Reliability
If the digital assistant pauses to process user interruption, then the user's feedback is captured, but the conversation flow is disrupted
Solution Approach 1:
The system performs preliminary action by continuously monitoring for user barge-in utterances during TTS playback and preparing to pause and process interruptions as they occur. This allows the system to capture user feedback at the appropriate moment without requiring significant delays, maintaining both response accuracy and conversation flow efficiency.
Solution Approach 2:
The system rushes through the interruption processing by detecting barge-in utterances and quickly pausing TTS output to process the user's input. This minimizes the time lost during interruptions while ensuring accurate response generation, allowing the conversation to resume smoothly after the interruption is processed.
3Loss of information
If the digital assistant generates a complete response, then the information is comprehensive, but the user may interrupt before receiving the full answer
Solution Approach 1:
The system dynamically adjusts the response generation process based on real-time user input. When a barge-in utterance is detected, the system pauses the current response generation and incorporates the user's input, then continues generating the complete response. This ensures both information completeness and smooth user interruption handling.
Solution Approach 2:
The system performs preliminary action by generating the complete response in advance and monitoring for user interruptions during playback. When a barge-in utterance is detected, the system pauses and processes the user's input, then resumes the response generation from the appropriate point, ensuring the complete information is delivered while handling interruptions naturally.
4Measurement precision
If the digital assistant uses traditional speech recognition, then the system is simple, but it cannot accurately determine which terms were spoken before interruption
Solution Approach 1:
The system segments the TTS output into individual terms or words and tracks the playback status of each segment. This segmentation allows the system to accurately determine which terms were spoken before a user interruption occurs. The complexity is managed by processing terms individually rather than treating the entire speech output as a single unit.
Solution Approach 2:
The system uses feedback from the speech recognition system to track which terms have been spoken during TTS playback. This feedback mechanism provides the information needed to accurately identify terms spoken before interruption while maintaining a relatively simple overall system architecture through efficient processing of the feedback data.
Data Source
AI summary
A method includes outputting, from an assistant-enabled device, a first text-to-speech (TTS) utterance generated from a first output transcription including a sequence of terms. While outputting the first TTS utterance from the assistant-enabled device, the method includes determining a corresponding playback status for each respective term of the sequence of terms, receiving a barge-in utterance spoken by a user, and identifying a subset of terms output from the assistant-enabled device before the user spoke the barge-in utterance based on the corresponding playback status of each respective term of the sequence of terms. The method also includes determining, based on the identified subset 10 of terms, a second output transcription responsive to the barge-in utterance spoken by the user. The method also includes outputting, from the assistant-enabled device, a second TTS utterance generated from the second output transcription.