Distributed Acoustic Model Training via Phonetic Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional speech recognition systems face limitations in effectively utilizing large amounts of training data and efficiently processing varying phonetic contexts, leading to suboptimal accuracy and impractical computational expenses.
Innovation Solution
A distributed computing approach is employed to train acoustic models by distributing data sequences of phonetic elements to multiple processing modules, where data for common sequences are transmitted to the same module, allowing efficient training of context-dependent Gaussian mixture models using large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition systems use large amounts of training data, then model accuracy can be improved, but computational expenses become impractical
Solution Approach 1:
The training data is segmented into different partitions based on phonetic contexts (triphones, bigrams, unigrams). Each processing module handles a specific subset of phonetic contexts, dividing the large computational task into manageable segments that can be processed in parallel across multiple modules.
Solution Approach 2:
The system introduces a distributed computing dimension by organizing processing modules across multiple computing nodes. Each module processes data for specific phonetic contexts independently, transforming a single-point computational bottleneck into a distributed parallel processing architecture that scales with hardware resources.
2Measurement precision
If traditional systems process varying phonetic contexts, then speech recognition accuracy improves, but processing efficiency decreases
Solution Approach 1:
Phonetic contexts are segmented into distinct categories (triphones, bigrams, unigrams) and assigned to different processing modules. This segmentation allows each module to specialize in specific context types, improving processing efficiency through targeted computation while maintaining comprehensive coverage of phonetic variations.
Solution Approach 2:
Each processing module is assigned specific phonetic contexts based on local requirements. The system dynamically determines which contexts each module should process, allowing optimized processing strategies for different phonetic types while maintaining overall system efficiency through coordinated specialization.
3Reliability
If data for all training sequences is transmitted to each processing module, then training completeness is ensured, but data transmission overhead increases
Solution Approach 1:
The training data is segmented and partitioned among different processing modules based on phonetic context types. Each module receives only the data relevant to its assigned contexts, eliminating redundant transmission of unrelated data while ensuring complete training coverage through the coordinated action of multiple specialized modules.
Solution Approach 2:
A data partitioning mechanism acts as an intermediary that intelligently distributes training data to appropriate processing modules. This intermediary layer ensures that each module receives precisely the data it needs for its assigned phonetic contexts, optimizing both transmission efficiency and training completeness.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training acoustic models. Speech data and data identifying a transcription for the speech data are received. A phonetic representation for the transcription is accessed. Training sequences are identified for a particular phone in the phonetic representation. Each of the training sequences includes a different set of contextual phones surrounding the particular phone. A partitioning key is identified based on a sequence of phones that occurs in each of the training sequences. A processing module to which the identified partitioning key is assigned is selected. Data identifying the training sequences and a portion of the speech data are transmitted to the selected processing module.