Encoder Neural Network Time Reduction Subnetwork
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Solution Overview
Problem
Current speech recognition systems face challenges in efficiently processing long acoustic sequences, leading to excessive computational requirements and memory usage, which hinders performance and scalability.
Innovation Solution
The implementation of an encoder neural network with a time reduction subnetwork, a convolutional LSTM subnetwork, and a network-in-network subnetwork, which reduces the time dimensionality of input sequences and adds depth of processing while managing parameter count, using techniques like concatenation and striding, batch normalization, and bidirectional LSTM layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the depth of processing is increased using more non-linearities and expressive power, then speech recognition performance is improved, but the number of parameters and computational footprint increases excessively
Solution Approach 1:
The encoder neural network is segmented into three distinct subnetworks: time reduction subnetwork, convolutional LSTM subnetwork, and network in network subnetwork. Each subnetwork performs a specific function (temporal downsampling, sequential processing with spatial features, and nested feature extraction), allowing the system to achieve deep processing while managing parameter complexity through functional decomposition
Solution Approach 2:
The time reduction subnetwork applies striding and concatenation techniques to reduce the temporal dimension of input sequences. By transforming the time dimension into a compressed representation, the system maintains essential temporal information while reducing the computational burden on subsequent layers, effectively adding processing depth without proportionally increasing parameter count
2Productivity
If the number of time steps in the input sequence is reduced, then processing efficiency is improved, but information loss may occur
Solution Approach 1:
The time reduction subnetwork combines multiple time steps through concatenation operations, merging acoustic features from adjacent time points into compressed representations. This merging process preserves essential acoustic information while reducing the total number of time steps, balancing information retention with processing efficiency
Solution Approach 2:
The network in network subnetwork implements nested feature extraction where smaller feature representations are embedded within larger contextual features. This nested structure allows the system to maintain detailed acoustic information at multiple levels of abstraction, preventing information loss during temporal compression
3Reliability
If convolutional LSTM and network in network subnetworks are added to increase computation per parameter, then performance is improved, but memory usage and processing resources increase
Solution Approach 1:
The convolutional LSTM subnetwork applies local spatial processing to acoustic features, using convolutional operations that process local temporal patterns with shared weights. This local quality approach increases computation per parameter by focusing processing power on relevant local features rather than uniformly processing all inputs, improving performance while controlling overall resource usage
Data Source
AI summary
A speech recognition neural network system includes an encoder neural network and a decoder neural network. The encoder neural network generates an encoded sequence from an input acoustic sequence that represents an utterance. The input acoustic sequence includes a respective acoustic feature representation at each of a plurality of input time steps, the encoded sequence includes a respective encoded representation at each of a plurality of time reduced time steps, and the number of time reduced time steps is less than the number of input time steps. The encoder neural network includes a time reduction subnetwork, a convolutional LSTM subnetwork, and a network in network subnetwork. The decoder neural network receives the encoded sequence and processes the encoded sequence to generate, for each position in an output sequence order, a set of sub string scores that includes a respective sub string score for each substring in a set of substrings.