Automatic Speech Recognition Acoustic Model Segmentation
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Solution Overview
Problem
Large vocabulary continuous speech recognition systems face a bottleneck due to the explosion in the number of parameters necessary for effective acoustic modeling, leading to resource-intensive solutions that are difficult to deploy on mobile platforms without compromising recognition accuracy.
Innovation Solution
The approach involves decoupling discrete and continuous representations of speech evidence using simple distortion models and a latent perception model, generating discrete and continuous parameterizations independently, and mapping these into a vector space to reduce dimensionality while minimizing information loss and model mismatch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If state-of-the-art acoustic modeling with high dimensionality is used, then recognition accuracy is improved, but resource consumption increases making deployment on mobile platforms difficult
Solution Approach 1:
The patent segments the acoustic model into multiple components: a context-dependent phoneme HMM layer, a context-independent phoneme HMM layer, and a tie-layer HMM. This segmentation allows the system to distribute the modeling task across multiple simpler models rather than relying on a single high-dimensional model, reducing the parameter burden while maintaining recognition accuracy.
Solution Approach 2:
The patent introduces a tie-layer HMM as an intermediary component between context-dependent and context-independent phoneme models. This tie-layer acts as a bridge that captures shared acoustic characteristics across different contexts, enabling the system to achieve accurate recognition with fewer parameters by leveraging the intermediary's condensed representation.
2Measurement precision
If the number of Gaussian components is increased to model acoustic variability, then recognition accuracy is improved, but computational complexity and memory requirements increase
Solution Approach 1:
The patent divides the acoustic modeling task into multiple HMM layers with different levels of context dependence. Each layer uses a manageable number of Gaussian components focused on specific aspects of acoustic variability, rather than requiring one massive layer with all possible variations. This segmentation reduces computational complexity while collectively capturing the full range of acoustic variability.
Solution Approach 2:
The patent transitions from modeling acoustic variability within a single high-dimensional space to modeling it across multiple lower-dimensional layers. By organizing the model hierarchically with context-dependent, context-independent, and tie-layers, the system achieves comprehensive coverage of acoustic variability through dimensional decomposition, reducing the computational burden of processing high-dimensional data in a single layer.
3Adaptability or versatility
If a large vocabulary is supported, then system versatility is improved, but the number of parameters required for acoustic modeling explodes
Solution Approach 1:
The patent segments the vocabulary modeling task by organizing acoustic units hierarchically across multiple HMM layers. The context-dependent layer handles phonemes with specific contextual information, the context-independent layer handles phonemes without context, and the tie-layer handles shared characteristics. This segmentation allows the system to support large vocabularies by distributing the parameter requirements across multiple specialized layers rather than requiring one enormous parameter set.
Solution Approach 2:
The patent addresses large vocabulary requirements by transitioning from a flat, single-layer parameter structure to a hierarchical multi-layer structure. Each layer operates in a lower-dimensional parameter space focused on specific linguistic levels (context-dependent phonemes, context-independent phonemes, and shared characteristics). This dimensional transformation enables the system to scale to large vocabularies by composing results from multiple lower-dimensional layers rather than requiring a single high-dimensional parameter space.
Data Source
AI summary
Exemplary embodiments of methods and apparatuses for automatic speech recognition are described. First model parameters associated with a first representation of an input signal are generated. The first representation of the input signal is a discrete parameter representation. Second model parameters associated with a second representation of the input signal are generated. The second representation of the input signal includes a continuous parameter representation of residuals of the input signal. The first representation of the input signal includes discrete parameters representing first portions of the input signal. The second representation includes discrete parameters representing second portions of the input signal that are smaller than the first portions. Third model parameters are generated to couple the first representation of the input signal with the second representation of the input signal. The first representation and the second representation of the input signal are mapped into a vector space.