Acoustic Modeling Neural Network Feedback Loop for Phoneme Prediction
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Solution Overview
Problem
Current acoustic modeling systems face challenges in accurately predicting phoneme representations for acoustic sequences due to the lack of explicit use of preceding outputs in the prediction process.
Innovation Solution
The proposed method involves using an acoustic modeling neural network that processes acoustic feature representations at each time step, combining the current acoustic feature representation with the output from the preceding time step to generate a modified input, which is then processed to improve the prediction of phoneme representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the acoustic modeling system processes each time step independently without incorporating preceding outputs, then the processing complexity is reduced, but the phoneme representation prediction accuracy deteriorates
Solution Approach 1:
The system incorporates feedback by feeding the output from the preceding time step back into the current time step processing. Specifically, the phoneme representation predicted at time step t-1 is used as additional input for predicting the phoneme representation at time step t, creating a recursive feedback loop that improves prediction accuracy by leveraging temporal dependencies in the acoustic sequence
Solution Approach 2:
The system performs preliminary action by pre-processing the acoustic sequence to extract feature representations before the main prediction process. Additionally, it prepares the preceding output in advance and combines it with the current acoustic features before feeding into the neural network, ensuring that temporal context is readily available when needed for prediction
2Reliability
If the system incorporates preceding outputs into the current time step input, then the phoneme classification accuracy is improved, but the computational requirements increase
Solution Approach 1:
The system applies partial action by selectively incorporating only the most relevant preceding output information into the current time step processing, rather than using all possible historical data. This selective approach maintains classification accuracy while reducing the computational burden of processing and storing extensive temporal sequences
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating representation of acoustic sequences. One of the methods includes: receiving an acoustic sequence, the acoustic sequence comprising a respective acoustic feature representation at each of a plurality of time steps; processing the acoustic feature representation at an initial time step using an acoustic modeling neural network; for each subsequent time step of the plurality of time steps: receiving an output generated by the acoustic modeling neural network for a preceding time step, generating a modified input from the output generated by the acoustic modeling neural network for the preceding time step and the acoustic representation for the time step, and processing the modified input using the acoustic modeling neural network to generate an output for the time step; and generating a phoneme representation for the utterance from the outputs for each of the time steps.