Phonetic Relationship Determination for Voice Command Distinction
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Solution Overview
Problem
Current speech recognition systems lack an efficient method to determine phonetic relationships between voice commands, leading to potential confusion between reserved and proposed voice commands, resulting in unintended operations and increased power consumption.
Innovation Solution
A computer-implemented method that converts input text phrases to audio outputs, then to text transcriptions, which are compared against a list of test phrases to determine phonetic similarity, ensuring voice commands are distinct from reserved commands, thereby reducing unnecessary operations and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition systems perform phonetic comparison between voice commands, then accuracy of command recognition is improved, but processing time and power consumption increase
Solution Approach 1:
The system pre-generates audio representations and transcriptions for all reserved voice commands during system initialization or idle periods. This preliminary preparation stores phonetic data in advance, so that when a user speaks a command, the system only needs to compare against pre-processed data rather than performing full audio analysis in real-time, thus reducing processing time while maintaining accuracy
Solution Approach 2:
The system replaces traditional audio-based phonetic comparison with text-based transcription comparison. By converting audio commands to text transcriptions and comparing textual representations rather than raw audio waveforms, the system achieves efficient phonetic distinction with significantly reduced processing requirements and faster execution
2Reliability
If speech recognition systems perform phonetic comparison between voice commands, then reliability of command execution is improved, but energy consumption increases
Solution Approach 1:
The system substitutes computationally intensive audio processing with efficient text string comparison. By converting voice commands to text transcriptions and comparing textual representations, the system maintains reliable phonetic distinction while consuming minimal processing energy, making it suitable for mobile and battery-powered devices
Solution Approach 2:
The system uses lightweight text transcription representations instead of heavy audio processing pipelines. These text-based representations require minimal computational resources to generate and compare, providing an energy-efficient solution that maintains reliability without the overhead of continuous audio analysis
3Measurement precision
If speech recognition systems perform real-time audio to text transcription, then phonetic relationship determination is improved, but device complexity increases
Solution Approach 1:
The system introduces text transcription as an intermediary layer between audio input and command interpretation. Instead of directly comparing audio waveforms, the system converts audio to text transcription and then compares textual representations, simplifying the overall system architecture while improving phonetic relationship determination accuracy
Data Source
AI summary
Systems and methods of determining phonetic relationships are provided. For instance data indicative of an input text phrase input by a user can be received. An audio output corresponding to a spoken rendering of the input text phrase can be determined. A text transcription of the audio output of the input text phrase can be determined. The text transcription can be a textual representation of the audio output. The text transcription can be compared against a plurality of test phrases to identify a match between the text transcription and at least one test phrase.