Recurrent Neural Network Audio Segmentation for Speech Recognition
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Solution Overview
Problem
Current speech recognition technologies face challenges in accurately distinguishing and separating speech from multiple speakers and background noise like silence and music, leading to reduced accuracy and erroneous recognition.
Innovation Solution
The use of a recurrent neural network (RNN) model to segment audio data into homogeneous segments by identifying change points, allowing for improved speaker diarization and subsequent accurate speech recognition by clustering audio features and assigning labels to each segment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition processes audio data as a whole, then the system is simpler to implement, but speech recognition accuracy deteriorates due to interference between multiple speakers and background noise
Solution Approach 1:
The patent segments audio data into multiple frames and further divides frames into segments based on speech feature cluster identifiers. This hierarchical segmentation allows the system to process different portions of audio data with appropriate methods, improving recognition accuracy by isolating speech from non-speech portions while managing computational complexity through structured organization
2Reliability
If the system processes all audio data uniformly, then processing is more efficient, but the system cannot distinguish between speech and non-speech elements, leading to erroneous recognition
Solution Approach 1:
The patent applies different processing qualities to different portions of audio data by identifying speech segments versus non-speech segments using speech feature cluster identifiers. Speech segments undergo detailed speech recognition processing while non-speech segments are handled differently, ensuring that each portion receives the appropriate level of processing attention and reliability
3Measurement precision
If the system uses advanced segmentation methods to separate speech from non-speech, then recognition accuracy improves, but computational time and resources increase
Solution Approach 1:
The patent performs preliminary segmentation of audio data into frames and identifies speech feature cluster identifiers before conducting full speech recognition. This preliminary action prepares the data in advance, allowing the main recognition process to focus only on relevant speech segments, thereby reducing overall processing time while maintaining high segmentation precision
Data Source
AI summary
Audio features, such as perceptual linear prediction (PLP) features and time derivatives thereof, are extracted from frames of training audio data including speech by multiple speakers, and silence, such as by using linear discriminant analysis (LDA). The frames are clustered into k-means clusters using distance measures, such as Mahalanobis distance measures, of means and variances of the extracted audio features of the frames. A recurrent neural network (RNN) is trained on the extracted audio features of the frames and cluster identifiers of the k-means clusters into which the frames have been clustered. The RNN is applied to audio data to segment audio data into segments that each correspond to one of the cluster identifiers. Each segment can be assigned a label corresponding to one of the cluster identifiers. Speech recognition can be performed on the segments.