Stacked ML Speech Analysis for Public Speaking
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Solution Overview
Problem
Current speech analysis systems are inefficient, inaccurate, and resource-intensive, making it difficult to provide effective feedback on speech quality, particularly for public speaking, as they require extensive resources and are expensive to train and deploy.
Innovation Solution
A stacked machine learning model comprising a deep convolutional neural network for feature aggregation, a long short-term memory model for sequence generation, and a deep neural network for decision-making is used to analyze speech audio data, enabling efficient and accurate detection of speech features such as filler words and disfluency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an automatic speech recognition (ASR) system is used to analyze speech features, then speech analysis capability is improved, but system size and resource requirements increase significantly
Solution Approach 1:
The patent extracts and focuses only on the specific speech features needed for public speaking analysis (filler words, disfluencies, pauses) rather than using a complete ASR system. This is achieved by training specialized machine learning models that directly detect these features from audio input, eliminating the need for full speech recognition functionality and significantly reducing system size while maintaining analysis capability for the target application.
2Measurement precision
If an automatic speech recognition (ASR) system is used to analyze speech features, then speech analysis capability is improved, but training and deployment costs increase
Solution Approach 1:
The patent employs lighter-weight machine learning models that are cheaper to train and deploy compared to full ASR systems. These models are designed to be computationally efficient and can be trained on specialized datasets for public speaking features without requiring the extensive resources needed for general-purpose speech recognition, making the solution more cost-effective for the specific application.
3Measurement precision
If an automatic speech recognition (ASR) system is used to analyze speech features, then speech analysis capability is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential processing components needed for detecting specific speech features (filler words, disfluencies, pauses) rather than performing complete speech recognition. This selective approach uses specialized models that process audio data more quickly by focusing only on relevant patterns, significantly reducing inference time while maintaining the ability to provide timely feedback for public speaking practice.
Data Source
AI summary
A method and system for detecting one or more speech features in speech audio data includes receiving speech audio data, performing preprocessing on the speech audio data to prepare the speech audio data for use as an input into one or more models that detect one or more speech features, providing the preprocessed speech audio data to a stacked machine learning model, and analyzing the preprocessed speech audio data via the stacked ML model to detect the one or more speech features. The stacked ML model includes a feature aggregation model, a sequence to sequence model, and a decision-making model.