Dynamic Pitch Adjustment for Speech Recognition Demographics
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Solution Overview
Problem
Conventional speech recognition systems face difficulties in accurately transcribing spoken input from certain demographics, such as young children, requiring specialized adaptation to improve recognition accuracy.
Innovation Solution
Dynamic pitch adjustment of inbound audio is implemented by detecting clusters of speech input, determining the average pitch, and adjusting it if necessary to enhance speech recognition accuracy, either locally on the device or through a network-connected system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition techniques are used, then the system is simple and universally applicable, but speech recognition accuracy deteriorates for certain demographics such as young children
Solution Approach 1:
The system dynamically adjusts the pitch of inbound audio in real-time based on detected speech characteristics. A pitch adjustment component modifies audio signals on-the-fly during speech recognition processing, allowing the system to adapt to different demographic speech patterns without requiring separate specialized systems for each demographic group.
Solution Approach 2:
The system changes the pitch parameter of the audio signal to optimize speech recognition accuracy. By detecting speech clusters and calculating average pitch, the system determines appropriate pitch adjustments and applies them to enhance recognition accuracy for different demographics, including young children whose speech patterns differ from adults.
2Measurement precision
If pitch adjustment is applied to all audio input, then speech recognition accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system applies pitch adjustment selectively rather than uniformly to all audio input. It detects speech clusters within the audio stream and applies pitch modification only to segments containing speech, using thresholds to determine when adjustment is necessary. This partial application reduces unnecessary processing while maintaining accuracy improvements where needed.
Solution Approach 2:
The audio stream is segmented into speech clusters and non-speech portions. The system identifies boundaries of speech segments and applies pitch adjustment only to relevant portions, rather than processing the entire audio stream uniformly. This segmentation approach reduces computational complexity by focusing processing resources on speech-containing segments only.
3Measurement precision
If specialized speech recognition components are created for specific demographics, then recognition accuracy improves for those groups, but system complexity and adaptation requirements increase
Solution Approach 1:
The pitch adjustment component serves multiple demographic groups simultaneously with a single unified system. Rather than creating separate specialized recognition components for children, adults, or other demographics, the system uses one adaptable component that dynamically adjusts pitch based on detected speech characteristics, making the same system effective across diverse user populations.
Data Source
AI summary
Aspects of the present disclosure relate to dynamic pitch adjustment of inbound audio to improve speech recognition. Inbound audio may be received. Upon receiving the inbound audio, clusters of speech input may be detected within the received inbound audio. An average pitch may be detected from the inbound audio, using either subparts of the inbound audio or one or more of the detected speech clusters. A determination may be made using, among other things, the average pitch. Based on this determination, the pitch of the inbound audio may be adjusted. The adjusted input may then be passed to a speech recognition component.