Hierarchical Recurrent Neural Network for Multi-Accent Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current speech recognition systems face challenges in accurately handling multiple pronunciations of words and inter-word co-articulation effects, particularly when dealing with different accents, as they rely on dictionaries that are not statistical models and fail to capture irregularities in spoken words.
Innovation Solution
A multi-accent speech recognition system is developed using a hierarchical recurrent neural network (HRNN) architecture that learns to align utterances with phonetic and grapheme transcriptions, trained in a multi-task learning setting with a multi-accent dataset, enabling direct prediction of grapheme representations and improving recognition accuracy across various dialects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional dictionary-based speech recognition is used, then the system is simple to implement, but it fails to capture irregularities in spoken words and multiple pronunciations across different accents
Solution Approach 1:
The patent replaces the traditional mechanical dictionary-based system with a neural network-based acoustic model that learns statistical patterns from data. The HRNN with CTC loss function substitutes the rule-based grapheme-to-phoneme conversion with a data-driven approach that automatically captures pronunciation variations across accents without requiring explicit programming of linguistic rules.
Solution Approach 2:
The patent changes the fundamental parameters of the speech recognition system by transitioning from fixed dictionary entries to learned neural network parameters. The model parameters are optimized through training on multi-accent data, allowing the system to adapt to different pronunciation patterns dynamically rather than relying on predetermined dictionary definitions.
2Adaptability or versatility
If phoneme-based acoustic models are used, then the model can capture spoken word variations, but it suffers from inter-word co-articulation effects and dialect-specific limitations
Solution Approach 1:
The patent introduces a hierarchical structure with multiple output layers operating at different levels of abstraction. The final grapheme-level output layer provides coarse alignment that is robust to accent variations, while intermediate phoneme-level layers capture finer pronunciation details. This multi-dimensional approach resolves the trade-off between accent robustness and alignment precision.
Solution Approach 2:
The HRNN architecture serves multiple functions simultaneously: it performs acoustic feature processing, phoneme prediction, grapheme prediction, and temporal alignment all within a single unified model. The shared hidden layers learn representations that are useful for both phoneme and grapheme prediction, making the model adaptable to different accents while maintaining precise alignment through the hierarchical output structure.
3Productivity
If grapheme-based acoustic models are used, then the model can directly predict grapheme representations, but non-hierarchical models fail to capture irregularities in spoken words
Solution Approach 1:
The patent segments the grapheme prediction task into multiple hierarchical stages. The final grapheme output layer handles direct grapheme prediction for speed, while intermediate phoneme layers break down complex grapheme sequences into manageable phoneme units that capture pronunciation irregularities. This segmentation allows the model to maintain both high productivity through direct grapheme output and high reliability through intermediate phoneme processing.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for training a hierarchical recurrent neural network (HRNN) having a plurality of parameters on a plurality of training acoustic sequences to generate phoneme representations of received acoustic sequences. One method includes, for each of the received training acoustic sequences: processing the received acoustic sequence in accordance with current values of the parameters of the HRNN to generate a predicted grapheme representation of the received acoustic sequence; processing an intermediate output generated by an intermediate layer of the HRNN during the processing of the received acoustic sequence to generate one or more predicted phoneme representations of the received acoustic sequence; and adjusting the current values of the parameters of the HRNN based at (i) the predicted grapheme representation and (ii) the one or more predicted phoneme representations.