ASR Acoustic Modeling With Multi-Task Embeddings for Noisy Speech
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Solution Overview
Problem
Conventional ASR systems struggle with adapting to varying environmental and speaker characteristics due to the limitations of single-task learned embeddings, leading to degraded speech recognition performance in noisy and reverberant conditions.
Innovation Solution
A Multi-Task Learned Embeddings (MTL) approach is employed, where a neural network is trained to extract embeddings encoded with environmental and speaker parameters, and a deep neural network is trained with a multi-task loss function to generate an acoustic model for improved adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-task learned embeddings (STL) are used for acoustic model adaptation, then the system is simpler to implement, but the adaptability to different environmental and speaker characteristics deteriorates
Solution Approach 1:
The patent implements multi-task learned embeddings that simultaneously learn multiple representations (speaker characteristics, environmental conditions, and speech content) within a single neural network model. This allows the acoustic model to adapt to different speakers and environments without requiring separate models, thus improving versatility while maintaining reasonable complexity.
Solution Approach 2:
The patent segments the embedding learning process into multiple task-specific branches within the neural network, where different branches learn different characteristics (speaker ID, environmental conditions, speech content). These segmented tasks are then integrated to form a comprehensive acoustic model that handles multiple adaptation scenarios.
2Reliability
If ASR models are trained using data containing specific environmental and speaker characteristics, then the model performs well on that specific data, but the reliability when applied to other characteristics deteriorates
Solution Approach 1:
The patent incorporates feedback mechanisms where the neural network continuously adjusts its embedding representations based on performance across multiple tasks. The multi-task loss function provides feedback signals that guide the model to learn features that are both task-specific and generally applicable, improving generalization to new environments and speakers.
Solution Approach 2:
The patent dynamically adjusts model parameters during training to optimize performance across multiple tasks simultaneously. By changing the weighting of different task losses and adapting embedding dimensions based on task requirements, the model achieves better reliability across diverse conditions while maintaining adaptability.
3Adaptability or versatility
If multi-task learned embeddings are used to improve adaptability, then the adaptability to different characteristics improves, but the device complexity increases
Solution Approach 1:
The patent merges multiple task-specific embedding layers into a unified multi-task embedding architecture. By combining speaker identification, environmental adaptation, and speech recognition tasks into a single integrated model with shared layers, the system achieves high adaptability while avoiding the complexity of maintaining separate models for each task.
Data Source
AI summary
State of the art Acoustic Models (AM), which are trained using data from one environment, may fail to adapt to another environment, and as a result, application is restricted. The disclosure herein generally relates to speech signal processing, and, more particularly, to a method and system for Automatic Speech Recognition (ASR) using Multi-task Learned Embeddings (MTL). In this approach, MTL embeddings are extracted from an MTL neural network that has been trained using feature vectors from a plurality of speech files. The MTL embeddings are then used for generating an acoustic model, which maybe then used for the purpose of Automatic Speech Recognition, along with the feature vectors and the MTL embeddings.