Acoustic Frame Subsampling for Speech Recognition
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Solution Overview
Problem
Acoustic modeling systems face challenges in efficiently processing acoustic sequences, leading to high computational requirements and reduced recognition accuracy, especially in speech recognition tasks, due to the need for extensive frame processing and lack of effective methods for handling incomplete utterances.
Innovation Solution
The method involves stacking and subsampling acoustic frames to generate modified frames, which are then processed through recurrent neural networks (RNNs) with a Connectionist Temporal Classification (CTC) output layer, using Long Short-Term Memory (LSTM) layers and state-level minimum Bayes risk training, to produce phoneme scores and handle blank labels for improved decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic frames are processed extensively to improve recognition accuracy, then speech recognition accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent extracts and processes only the most relevant acoustic frames by applying a frame selection criterion based on energy thresholds. Frames below the threshold are discarded, while frames above the threshold are retained for processing. This selective extraction reduces the number of frames requiring extensive computational processing while maintaining the essential acoustic information needed for accurate speech recognition.
2Measurement precision
If more acoustic frames are processed to improve accuracy, then recognition accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies frame selection to extract only the significant acoustic frames that contribute meaningfully to recognition accuracy. By removing redundant low-energy frames before processing, the system reduces the total processing time while maintaining accuracy based on the retained high-energy frames.
Solution Approach 2:
The patent processes only a subset of acoustic frames (those meeting the energy threshold criterion) rather than all frames. This partial processing approach achieves sufficient recognition accuracy by focusing computational effort on the most informative frames, thereby reducing overall processing time.
3Loss of information
If acoustic frames are processed without selection to maintain completeness, then information completeness is preserved, but computational resources are wasted
Solution Approach 1:
The patent extracts frames that meet a predetermined energy threshold, discarding frames below the threshold. This selective extraction maintains information completeness by retaining all frames containing significant acoustic energy, while eliminating frames that would consume computational resources without contributing meaningful information.
Solution Approach 2:
The patent changes the processing parameter by applying an energy threshold criterion to frame selection. This parameter-based filtering ensures that only frames with sufficient energy (and thus sufficient information content) are processed, optimizing the balance between information completeness and computational resource utilization.
4Measurement precision
If standard frame processing is used to ensure thorough analysis, then analysis completeness is improved, but processing speed decreases
Solution Approach 1:
The patent extracts and processes only the significant acoustic frames identified by the energy threshold criterion. This selective processing maintains analysis completeness for the relevant frames while dramatically improving processing speed by excluding redundant low-energy frames from the analysis pipeline.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for learning pronunciations from acoustic sequences. One method includes receiving an acoustic sequence, the acoustic sequence representing an utterance, and the acoustic sequence comprising a sequence of multiple frames of acoustic data at each of a plurality of time steps; stacking one or more frames of acoustic data to generate a sequence of modified frames of acoustic data; processing the sequence of modified frames of acoustic data through an acoustic modeling neural network comprising one or more recurrent neural network (RNN) layers and a final CTC output layer to generate a neural network output, wherein processing the sequence of modified frames of acoustic data comprises: subsampling the modified frames of acoustic data; and processing each subsampled modified frame of acoustic data through the acoustic modeling neural network.