Lattice-Based Querying for Speech Recognition Ambiguity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech-recognition technologies face challenges in handling open-ended queries and 'out-of-vocabulary' terms, leading to ineffective information retrieval due to limitations in language models and lack of flexibility in handling ambiguities and errors.
Innovation Solution
A computer system processes audio speech queries by generating a lattice representation with multiple alternative phrase sequences, comparing these to documents, and determining matching scores to provide a ranking of relevant results, accommodating ambiguities and uncertainties through weighted paths and penalties for errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a speech-recognition engine uses a single best-guess word sequence, then the recognition process is simple and fast, but the system lacks flexibility and cannot handle ambiguities or errors effectively
Solution Approach 1:
The system transitions from a static single-sequence approach to a dynamic lattice structure that can adapt to different query types and ambiguities. The lattice allows the system to flexibly represent multiple alternative interpretations of spoken queries, enabling it to handle open-ended domains and out-of-vocabulary terms effectively.
Solution Approach 2:
The system changes the parameter of representation from a single word sequence to a lattice structure with multiple paths, each having associated weights. This parameter change allows the system to capture uncertainties and ambiguities in speech recognition while maintaining computational tractability through weighted scoring.
2Device complexity
If a speech-recognition engine uses traditional language models with fixed vocabulary, then the model structure is simple, but it cannot accommodate out-of-vocabulary terms or open-ended queries
Solution Approach 1:
The system segments the query processing into distinct phases: generating alternative phrase sequences, scoring them against documents, and ranking results. This segmentation allows the use of simple language models for vocabulary lookup while handling out-of-vocabulary terms through alternative phrase generation and scoring mechanisms.
Solution Approach 2:
The lattice structure acts as an intermediary between the simple language model and the complex task of handling open-ended queries. It generates alternative phrase sequences that bridge the gap between fixed vocabulary models and out-of-vocabulary terms, enabling flexible query processing without requiring complex model architecture.
3Reliability
If the system generates multiple alternative phrase sequences with weights, then the system can handle ambiguities and errors, but the processing complexity increases
Solution Approach 1:
The system generates multiple alternative phrase sequences beyond what a single-best approach would produce, but not exhaustively all possible sequences. This partial action provides sufficient redundancy to handle ambiguities and errors while keeping processing complexity manageable through selective generation and scoring.
4Measurement precision
If the system compares multiple paths to documents with weighted scoring, then the search results are more accurate and relevant, but the computation time increases
Solution Approach 1:
The system changes the scoring parameter to include weights associated with each path in the lattice, allowing more precise matching scores that reflect confidence levels and alternative interpretations. This parameter enhancement improves result accuracy while the modular scoring approach keeps computation manageable by processing paths independently.
Data Source
AI summary
A system for processing a query operates by receiving a first query segment that includes audio speech. Next, the system generates a representation for this first query segment, where the representation includes at least two paths associated with alternative phrase sequences for an ambiguity in the audio speech. The system then compares the paths in the representation to a group of documents and determines matching scores for the group of documents based on the comparisons. Finally, the system presents a ranking of the group of documents, where the ranking is based on the matching scores for the group of documents.