Deep neural networks (DNNs) based automatic speech recognition (ASR) systems are often designed using expert knowledge and empirical evaluation. In this paper, a range of neural architecture search (NAS) techniques are used to automatically learn two  hyper-parameters that heavily affect the performance and model complexity of state-of-the-art factored time delay neural network (TDNN-F)acoustic models : i) the left and right splicing context offsets; and ii) the dimensionality of the bottleneck linear projection at each hidden layer. These include the standard DARTS method fully integrating the estimation of architecture weights and TDNN parameters in lattice-free MMI (LF-MMI)  training; Gumbel-Softmax DARTS that reduces the confusion between candidate architectures ; Pipelined DARTS that circumvents the overfitting of architecture weights using held-out data ; and Penalized DARTS that further incorporates resource constraints to adjust the trade-off between performance and system complexity. Parameter sharing among candidate architectures was also used to facilitate efficient search over up to 7^{28} different TDNN systems. Experiments conducted on a 300-hour Switchboard conversational telephone speech recognition task suggest the NAS auto-configured TDNN-F systems consistently outperform the baseline LF-MMI trained TDNN-F systems using manual expert configurations . Absolute word error rate  reductions up to 1.0 \% and relative model size reduction of 28\% were obtained.%DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > and relative model size reduction of 28\\% were obtained. Consistent performance improvements were also obtained on a UASpeech disordered speech recognition task using the proposed NAS approaches.
Deep neural networks (DNNs) based automatic speech recognition (ASR) systems are often designed using expert knowledge and empirical evaluation. In this paper, a range of neural architecture search (NAS) techniques are used to automatically learn two types of hyper-parameters of state-of-the-art factored time delay neural networks (TDNNs) : i) the left and right splicing context offsets; and ii) the dimensionality of the bottleneck linear projection at each hidden layer. These include the DARTS method integrating architecture selection with lattice-free MMI (LF-MMI) TDNN training; Gumbel-Softmax and pipelined DARTS reducing the confusion over candidate architectures and improving the generalization of architecture selection ; and Penalized DARTS incorporating resource constraints to adjust the trade-off between performance and system complexity. Parameter sharing among candidate architectures allows efficient search over up to 7^{28} different TDNN systems. Experiments conducted on the 300-hour Switchboard corpus suggest the auto-configured systems consistently outperform the baseline LF-MMI TDNN systems using manual network design or random architecture search after LHUC speaker adaptation and RNNLM rescoring . Absolute word error rate (WER) reductions up to 1.0 %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > and relative model size reduction of 28\\% were obtained. Consistent performance improvements were also obtained on a UASpeech disordered speech recognition task using the proposed NAS approaches.
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types of
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that heavily affect the performance and model complexity
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network (TDNN-F)acoustic models
networks (TDNNs)
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standard DARTS method fully integrating the estimation of architecture weights and TDNN parameters in
DARTS method integrating architecture selection with
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TDNN
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DARTS that reduces the confusion between candidate architectures ; Pipelined DARTS that circumvents the overfitting of architecture weights using held-out data
and pipelined DARTS reducing the confusion over candidate architectures and improving the generalization of architecture selection
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that further incorporates
incorporating
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was also used to facilitate
allows
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a
the
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conversational telephone speech recognition task suggest the NAS
corpus suggest the
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TDNN-F
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trained TDNN-F
TDNN
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expert configurations
network design or random architecture search after LHUC speaker adaptation and RNNLM rescoring
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(WER)
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\% and relative model size reduction of 28\% were obtained.