State-of-the-art end-to-end automatic speech recognition (ASR) extracts acoustic features from input speech signal every 10 ms which corresponds to a frame rate of 100 frames second. In this report, we investigate the use of high-frame-rate features extraction in end-to-end ASR. High frame rates of 200 and 400 frames second are used in the features extraction and provide additional information for end-to-end ASR. The effectiveness of high-frame-rate features extraction is evaluated independently and in combination with speed perturbation based data augmentation. Experiments performed on two speech corpora, Wall Street Journal (WSJ) and CHiME-5, show that using high-frame-rate features extraction yields improved performance for end-to-end ASR, both independently and in combination with speed perturbation. On WSJ corpus, the relative reduction of word error rate (WER) yielded by high-frame-rate features extraction independently and in combination with speed perturbation are up to 21.3 and 24.1 , respectively. On CHiME-5 corpus, the corresponding relative WER reductions are up to 2.8 and 7.9 , respectively, on the test data recorded by microphone arrays and up to 11.8 and 21.2 , respectively, on the test data recorded by binaural microphones.
Speed perturbation @cite_5 is a data augmentation technique which creates warped time signals in addition to the original speech signals. Given an audio signal of length @math and a warping factor @math , speed perturbation creates a new signal with duration @math by resampling the original signal with a sampling rate of @math , where @math is the sampling rate of the original signal. Speed perturbation shifts the speech spectrum and also results in change in number of frames as the duration of the resulting signal is different @cite_5 .
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Data augmentation is a common strategy adopted to increase the quantity of training data, avoid overfitting and improve robustness of the models. In this paper, we investigate audio-level speech augmentation methods which directly process the raw signal. The method we particularly recommend is to change the speed of the audio signal, producing 3 versions of the original signal with speed factors of 0.9, 1.0 and 1.1. The proposed technique has a low implementation cost, making it easy to adopt. We present results on 4 different LVCSR tasks with training data ranging from 100 hours to 1000 hours, to examine the effectiveness of audio augmentation in a variety of data scenarios. An average relative improvement of 4.3 was observed across the 4 tasks.