Modulation Spectrum Features for Robust Automatic Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing Automatic Speech Recognition (ASR) models face challenges in accuracy due to background noise, accents, and variations in speaking styles, making them less robust in real-world conditions, and training these models is expensive and requires vast amounts of labeled data.
Innovation Solution
Implementing a modulation spectrum-based architecture for ASR models by extracting a modulation spectrum from the output of an encoder, normalizing and applying ReLU nonlinearity, and using convolution filters to enhance the input to the decoder, thereby improving the model's accuracy without significant additional cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ASR models are used, then the system structure is simple, but the recognition accuracy is low due to background noise, accents, and variations in speaking styles
Solution Approach 1:
The patent introduces a modulation spectrum dimension by applying Fourier transforms to the temporal dimension of encoder outputs, creating a time-frequency representation that captures speech dynamics. This dimensional transformation enables the decoder to access spectral information that improves recognition accuracy without fundamentally redesigning the entire model architecture.
Solution Approach 2:
The modulation spectrum acts as an intermediary representation between the encoder and decoder. By transforming encoder outputs into the modulation spectrum domain and then back to time domain, the system mediates information flow to enhance speech features while maintaining architectural simplicity through reusable transformer components.
2Measurement precision
If more labeled data is collected to improve accuracy, then recognition performance improves, but the cost and time required for data collection and annotation increase
Solution Approach 1:
The modulation spectrum extraction process is self-service in that it automatically generates enhanced features from existing encoder outputs without requiring additional labeled data. The Fourier transform and inverse Fourier transform operations are performed automatically during model inference, enabling the system to improve its own feature representation without external data annotation efforts.
3Reliability
If the model is trained to handle various speech conditions, then robustness improves, but training cost and computational resources increase
Solution Approach 1:
The patent changes the parameter representation by introducing modulation spectrum features that capture temporal dynamics in the frequency domain. This parameter transformation enables the model to better distinguish speech patterns under varying conditions (noise, accents, speaking styles) without requiring extensive retraining, as the modulation spectrum provides a more discriminative feature space.
Data Source
AI summary
Techniques for speech recognition models using modulation spectrum are disclosed herein. A modulation spectrum is generated from time series data output of an encoder layer of a speech recognition model and used as input into a decoder layer of the speech recognition model to improve accuracy of the model such as for recognizing subword units. The modulation spectrum is determined by applying a convolution filter to the output of the encoder layer of the speech recognition model. The time series data and/or the modulation spectrum can be normalized. A rectified linear unit activation function can be applied to the output of the convolution filter. The output of the encoder layer may be residually connected to the output of the rectified linear unit activation function prior to being input into the decoder layer.