Automatic Domain Adaptation for Speech Recognition

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Generic speech recognition models face challenges in recognizing out-of-vocabulary words, which are common in specific domains, leading to decreased performance and requiring lengthy manual adaptation processes.

Innovation Solution

An automatic language adaptation system that identifies and augments the language model with domain-specific terms by analyzing interactions and external resources, updating the lexicon and n-gram probabilities to improve recognition accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a generic speech recognition model is used, then the system can handle general vocabulary, but it fails to recognize domain-specific out-of-vocabulary words leading to low performance

Engineering Contradiction:

Improvevocabulary coverageVSAvoidrecognition accuracy

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary action by automatically collecting domain-specific documents and extracting terminology before the speech recognition task. This advance preparation builds a customized vocabulary database that enables the model to recognize domain-specific terms without requiring manual pre-training, thus resolving the contradiction between handling general vocabulary and recognizing domain-specific out-of-vocabulary words.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If manual transcription of masses of data is performed to adapt the generic model to specific jargon, then the model can be trained to recognize domain-specific terms, but the process is lengthy and time-consuming

Engineering Contradiction:

Improvemodel adaptation qualityVSAvoidadaptation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system replaces the mechanical manual transcription process with an automated computational system. The automated terminology extraction system uses natural language processing and text mining techniques to automatically collect, process, and extract domain-specific terms from documents, eliminating the need for manual transcription while maintaining high adaptation quality and significantly reducing time consumption.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements self-service by automatically performing the entire model adaptation process without human intervention. It autonomously collects domain documents, extracts terminology, updates the language model, and integrates the customized vocabulary, enabling the speech recognition system to adapt to domain-specific jargon independently and efficiently.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If the language model is augmented with domain-specific terms, then recognition accuracy for unique jargon improves, but the complexity of the adaptation process increases

Engineering Contradiction:

Improverecognition accuracyVSAvoidadaptation process complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system applies segmentation by breaking down the complex adaptation process into distinct modular stages: document collection, text preprocessing, terminology extraction, vocabulary database construction, and language model integration. Each module performs a specific function and can be independently configured or adjusted, simplifying the overall process complexity while maintaining high recognition accuracy for domain-specific terms.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS8996371B2Method and system for automatic domain adaptation in speech recognition applications

Publication Date: 2015.03.31 NICE LTD

AI summary

A system and method for adapting a language model to a specific environment by receiving interactions captured the specific environment, generating a collection of documents from documents retrieved from external resources, detecting in the collection of documents terms related to the environment that are not included in an initial language model and adapting the initial language model to include the terms detected.