Voice Input Textual Identifier Recognition System
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Solution Overview
Problem
Digital assistant systems struggle to recognize and process special types of text, such as TWITTER hashtags and usernames, when receiving voice inputs, leading to ambiguous transcriptions that fail to capture the user's intended formatting.
Innovation Solution
The system identifies keywords within voice inputs to determine if adjacent words form textual identifiers like hashtags or usernames, and replaces them with appropriate formatting, using symbols like '#' for hashtags and '@' for usernames, based on predefined collections or popularity metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional speech-to-text systems transcribe voice input directly, then the transcription process is simple and fast, but the system cannot recognize special text formats like hashtags and usernames
Solution Approach 1:
The system segments the transcription process into multiple stages: initial speech-to-text conversion, keyword identification, contextual analysis, and format application. This segmentation allows the system to handle special text formats without completely redesigning the speech-to-text engine, thus improving adaptability while managing complexity.
Solution Approach 2:
The system introduces an intermediary processing layer between speech-to-text conversion and final output. This intermediary layer analyzes the transcribed text for keywords indicating special formats (hashtags, usernames) and applies appropriate formatting rules, enabling format recognition without direct modification of the core speech-to-text system.
2Measurement precision
If the system applies format recognition to all voice inputs, then special text formats are accurately recognized, but processing time increases
Solution Approach 1:
The system applies format recognition selectively rather than universally. It identifies keywords associated with special formats (e.g., 'hashtag', 'username') and applies formatting only when these keywords are detected, avoiding unnecessary processing of regular text and thus reducing overall processing time while maintaining high recognition accuracy for relevant cases.
Solution Approach 2:
The system performs preliminary keyword identification during the transcription process itself, rather than analyzing the entire transcribed text afterward. By detecting format-indicating keywords early in the processing pipeline, the system can apply formatting rules immediately without waiting for complete transcription, reducing overall processing time.
3Reliability
If the system replaces keywords with formatted text, then the output matches user intent, but ambiguity remains when keywords appear in regular text contexts
Solution Approach 1:
The system applies different processing rules to different parts of the transcribed text based on local context. When a keyword like 'hashtag' appears, the system analyzes the surrounding words to determine if they form a valid hashtag pattern (capitalization, relevance to context) before applying formatting. This local quality analysis ensures reliable intent interpretation while avoiding false formatting in regular text contexts.
Solution Approach 2:
The system incorporates feedback mechanisms to verify whether detected keywords should trigger format application. It checks if the surrounding words match expected patterns for special formats and whether the formatting makes contextual sense before replacing the keyword. This feedback loop resolves contextual ambiguity by confirming user intent before applying formatting transformations.
Data Source
AI summary
Methods and systems for recognizing textual identifiers within a plurality of words are described. A textual representation of a voice input is received from a user. The textual representation includes a plurality of words. A keyword is identified in the textual representation. It is determined whether one or more words adjacent to the keyword correspond to a textual identifier of a collection of textual identifiers. Responsive to a determination that the one or more adjacent words correspond to a textual identifier, the keyword and the one or more adjacent words are replaced with the textual identifier.