Speech Recognition Framework Using Spectrogram Augmentation
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Solution Overview
Problem
Conventional speech recognition models require large amounts of data for training, are resource-intensive, and struggle with domain adaptation, leading to poor performance in different voice types and environmental noise conditions.
Innovation Solution
A novel framework that includes spectrogram augmentation, a feature encoder, a parameter-efficient acoustic model, and a masked correction module, utilizing self-attention and convolution layers, to improve speech recognition with reduced data and computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional speech recognition models are used, then speech recognition functionality is provided, but large amounts of training data and computing resources are required
Solution Approach 1:
The system performs preliminary actions by pre-processing audio data into spectrograms and pre-augmenting them with various transformations (adding noise, reverberation, pitch shifts) before the main speech recognition task. This preliminary preparation enables the model to learn from more varied data representations without requiring proportionally more raw training data.
Solution Approach 2:
The patent introduces spectrograms as an intermediary representation between raw audio signals and the speech recognition model. Instead of feeding raw audio directly, the system converts audio to spectrogram format and applies augmentations in this intermediate domain, which improves data efficiency and model performance.
2Reliability
If conventional speech recognition models are used, then speech recognition is provided, but resource-intensive computing is required
Solution Approach 1:
The system segments the speech recognition task into distinct processing stages: audio-to-spectrogram conversion, spectrogram augmentation, feature extraction, and recognition. This segmentation allows for optimized processing at each stage, reducing overall computational burden while maintaining accuracy.
Solution Approach 2:
The patent applies parameter changes by transforming spectrograms through various augmentations (noise addition, reverberation, pitch manipulation) that modify the input data characteristics. These parameter transformations enable the model to generalize better from fewer training examples, reducing the computational resources needed for training and inference.
3Adaptability or versatility
If conventional speech recognition models are used, then basic speech recognition is provided, but poor performance occurs in different voice types and environmental noise conditions
Solution Approach 1:
The system introduces dynamics by applying multiple types of augmentations (noise, reverberation, pitch shifts, volume changes) to the spectrograms during training. This dynamic transformation of training data enables the model to adapt to various voice types and environmental conditions, improving reliability across different scenarios.
Solution Approach 2:
The patent creates a universal speech recognition system by training with augmented spectrograms that simulate diverse conditions (different noises, reverberation levels, pitch variations). This multi-functional training approach enables the single model to perform reliably across multiple voice types and environmental conditions without requiring separate models for each scenario.
Data Source
AI summary
A method for managing a technical support conversation includes: obtaining, by an analyzer, an audio data and a transcript; converting, by the analyzer, the audio data to a Mel spectrogram (MS), in which the MS is provided to an augmentation module (AM) and the transcript is provided to a feature encoder; augmenting, by the AM, the MS to obtain an augmented MS, in which the augmented MS is provided to the feature encoder; analyzing, by the feature encoder, the augmented MS and transcript to generate feature vectors (FVs), in which the FVs are provided to an engine; analyzing, by the engine, the FVs to train a model; training, by the engine and using the FVs, the model to generate a trained model based on a target parameter; and initiating, by the engine, notification of an administrator about the trained model.