Speech Recognition Dictionary Refinement for Spontaneous Speech

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Solution Overview

Problem

Existing speech recognition systems face challenges in accurately recognizing spontaneous speech due to informalities, errors, and non-standard constructions, which affects their reliability and performance.

Innovation Solution

A refinement procedure is implemented using a dictionary refinement manager that identifies problematic words, generates pronunciation candidates, and optimizes them through a phonetic recognizer and sequence analyzer, combining these with non-problematic pronunciations to create a refined speech recognition dictionary.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If speech recognition systems are designed to handle spontaneous speech with informalities and errors, then recognition accuracy improves, but system complexity increases

Engineering Contradiction:

Improverecognition accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The speech recognition system is divided into multiple specialized modules: acoustic model for sound processing, language model for grammatical analysis, dictionary refinement manager for vocabulary optimization, and confidence score generator for reliability assessment. Each module handles specific aspects of spontaneous speech processing, improving overall accuracy without requiring complete system redesign.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The dictionary refinement manager performs preliminary processing by analyzing problematic words, generating pronunciation candidates, and optimizing the speech dictionary before recognition operations. This pre-processing step anticipates spontaneous speech variations and prepares appropriate handling strategies in advance, reducing the burden during actual recognition.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If more processing power and hardware resources are allocated to enhance speech recognition capability, then system performance improves, but production costs and operational inefficiencies increase

Engineering Contradiction:

Improvesystem performanceVSAvoidproduction costs

Core Design Contradiction:

ProductivityVSEase of manufacture

Solution Approach 1:

The system applies partial processing strategies by focusing computational resources on problematic words and high-confidence recognition tasks. The dictionary refinement manager selectively optimizes only the most challenging vocabulary items rather than processing the entire dictionary uniformly, reducing overall computational requirements while maintaining performance.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system dynamically adjusts recognition parameters such as confidence thresholds, dictionary selection criteria, and processing depth based on input characteristics. This allows the system to optimize performance for each specific recognition task without requiring maximum hardware resources for all operations, improving operational efficiency.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If the speech recognition dictionary is expanded to include more vocabulary words and pronunciations, then recognition coverage improves, but processing time and computational load increase

Engineering Contradiction:

Improverecognition coverageVSAvoidprocessing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The dictionary refinement manager performs preliminary organization and optimization of the speech dictionary by categorizing words, pre-computing pronunciation variants, and structuring data for efficient retrieval. This pre-processing enables faster access during recognition operations, allowing comprehensive vocabulary coverage without proportional increases in processing time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The expanded dictionary is segmented into multiple organized sections: common words, problematic words, domain-specific vocabulary, and pronunciation variants. This segmentation allows the system to selectively access only relevant portions during recognition rather than searching the entire dictionary, maintaining fast processing times despite comprehensive coverage.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS7272560B2Methodology for performing a refinement procedure to implement a speech recognition dictionary

Publication Date: 2007.09.18 SONY GROUP CORP

AI summary

A system and method for performing a refinement procedure to effectively implement a speech recognition dictionary for spontaneous speech recognition may include a problematic word identifier configured to divide vocabulary words from an initial speech recognition dictionary into problematic words and non-problematic words according to pre-defined identification criteria. A candidate generator may analyze the problematic words to produce one or more pronunciation candidates for each of the problematic words. An optimization module may then perform an optimization process for refining one or more pronunciation candidates according to certain optimization criteria to thereby generate optimized problematic pronunciations. A dictionary refinement manager may finally combine the optimized problematic pronunciations with non-problematic pronunciations of the non-problematic words to produce a refined speech recognition dictionary for use by the speech recognition system.