Leveraging End-to-End Speech Recognition with Neural Architecture Search

• URL: http://arxiv.org/abs/1912.05946v2
• Date: Sat, 20 May 2023 23:27:51 GMT
• Title: Leveraging End-to-End Speech Recognition with Neural Architecture Search
• Authors: Ahmed Baruwa, Mojeed Abisiga, Ibrahim Gbadegesin, Afeez Fakunle
• Abstract summary: We show that a large improvement in the accuracy of deep speech models can be achieved with effective Neural Architecture Optimization. Our method achieves test error of 7% Word Error Rate (WER) on the LibriSpeech corpus and 13% Phone Error Rate (PER) on the TIMIT corpus, on par with state-of-the-art results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep neural networks (DNNs) have been demonstrated to outperform many traditional machine learning algorithms in Automatic Speech Recognition (ASR). In this paper, we show that a large improvement in the accuracy of deep speech models can be achieved with effective Neural Architecture Optimization at a very low computational cost. Phone recognition tests with the popular LibriSpeech and TIMIT benchmarks proved this fact by displaying the ability to discover and train novel candidate models within a few hours (less than a day) many times faster than the attention-based seq2seq models. Our method achieves test error of 7% Word Error Rate (WER) on the LibriSpeech corpus and 13% Phone Error Rate (PER) on the TIMIT corpus, on par with state-of-the-art results.

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