Hint Word Generation for ASR Accuracy
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Solution Overview
Problem
Automated Speech Recognition (ASR) systems face limitations in accuracy due to difficulties with words that sound identical or similar, mispronounced words, and noise in speech input signals, leading to user frustration.
Innovation Solution
Generating hint words that provide contextual information to ASR systems by selecting frequently occurring terms from user interactions, such as search queries and commands, and using graph data structures to identify proximate terms, which are then transmitted to assist in speech-to-text conversions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ASR systems process speech input without additional context, then the processing speed is fast, but the accuracy decreases due to homophones, mispronunciations, and noise
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user interaction data (search queries, commands, corrections) before the actual speech recognition process. This pre-processing creates a personalized context model that is then applied during speech recognition to improve accuracy without adding complexity to the core ASR engine.
Solution Approach 2:
The patent introduces an intermediary component (context generation system) that sits between the speech input and the ASR engine. This intermediary processes user interaction data to generate contextual information, which then guides the ASR recognition process, effectively mediating between raw speech and interpretation to resolve ambiguities.
2Adaptability or versatility
If hint words are collected from all user interactions, then the context coverage is comprehensive, but the data processing time increases
Solution Approach 1:
The system extracts only the most relevant features from user interaction data - specifically focusing on search queries, commands, and correction patterns - rather than processing all possible interaction types. This selective extraction maintains comprehensive context coverage while significantly reducing processing time by filtering out less relevant data.
Solution Approach 2:
The patent applies partial action by using a subset of user interaction data (specific types like searches and commands) rather than all possible interactions. This partial approach provides sufficient context for accurate recognition without the overhead of processing every possible user action, achieving the right balance between coverage and efficiency.
3Measurement precision
If a large number of hint words are provided to ASR, then the context information is rich, but the computational load increases
Solution Approach 1:
The system dynamically changes parameters by adjusting the number and type of hint words provided based on the specific speech input context. Rather than always providing a fixed large set of hint words, the system adapts the hint word set size and composition to match the ambiguity level of the input, reducing computational energy when less context is needed while maintaining accuracy when ambiguity is high.
Data Source
AI summary
Systems and methods for determining hint words that improve the accuracy of automated speech recognition (ASR) systems. Hint words are determined in the context of a user issuing voice commands in connection with a voice interface system. Terms are initially taken from most frequently occurring terms in operation of a voice interface system. For example, most frequently occurring terms that arise in electronic search queries or received commands are selected. Certain of these terms are selected as hint words, and the selected hint words are then transmitted to an ASR system to assist in translation of speech to text.