Context-Dependent Language Model Clustering for ASR
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing language models for automatic speech recognition (ASR) lack accuracy due to the manual and assumption-based approach of clustering training data based on contextual information, which can lead to suboptimal performance.
Innovation Solution
The method involves obtaining language data with associated metadata attributes, identifying relevant metadata attributes for clustering, and generating distinct language models for each cluster, allowing for dynamic contextual analysis and improved ASR accuracy by matching new voice utterances with corresponding clusters based on their metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual clustering of training data based on contextual information is used, then the language model can be generated with contextual awareness, but the accuracy is reduced due to assumption-based approach and suboptimal performance
Solution Approach 1:
The patent implements dynamic clustering by automatically identifying relevant metadata attributes and their weights based on the training data itself, rather than using fixed manual assumptions. The system dynamically adapts the clustering strategy by computing optimal attribute weights through iterative processes, allowing the language model to dynamically adjust to different contextual patterns in the data, thereby resolving the contradiction between manual contextual awareness and actual accuracy.
2Reliability
If multiple language models are generated for different clusters, then the recognition performance is improved, but the system complexity increases
Solution Approach 1:
The patent manages system complexity by dynamically adjusting parameters such as the number of clusters, metadata attribute weights, and cluster selection criteria based on the specific dataset and application requirements. Rather than always generating multiple complex models, the system can adaptively simplify to fewer clusters or adjust attribute importance weights, allowing flexibility in balancing recognition performance against system complexity depending on the given data characteristics.
Data Source
AI summary
Techniques for generating language models. The techniques include: obtaining language data comprising training data and associated values for one or more metadata attributes, the language data comprising a plurality of instances of language data, an instance of language data comprising an instance of training data and one or more metadata attribute values associated with the instance of training data; identifying, by processing the language data using at least one processor, a set of one or more of the metadata attributes to use for clustering the instances of training data into a plurality of clusters; clustering the training data instances based on their respective values for the identified set of metadata attributes into the plurality of clusters; and generating a language model for each of the plurality of clusters.