计算频谱的传统方法：在端到端ASR中的应用
Radically Old Way of Computing Spectra: Applications in End-to-End ASR
摘要：
我们提出了一种使用频域线性预测（FDLP）来计算频谱图的技术，该技术使用全极点模型来拟合不同频率子带中语音的平方希尔伯特包络。一个完整的语音话语的频谱图是通过连续的全极点模型响应的叠加计算的。1.5秒的长上下文窗口使我们能够捕获频谱图中语音的低频时间调制。对于端到端的自动语音识别任务，FDLP频谱图与用于干净读取语音训练和测试数据的标准mel频谱图特征表现相当。对于具有训练测试域失配或混响的更真实的语音数据，FDLP谱图分别显示出相对于mel谱图的25%和22%的相对WER改进。
英文摘要：
We propose a technique to compute spectrograms using Frequency Domain Linear Prediction (FDLP) that uses all-pole models to fit the squared Hilbert envelope of speech in different frequency sub-bands. The spectrogram of a complete speech utterance is computed by overlap-add of contiguous all-pole model responses. A long context window of 1.5 seconds allows us to capture the low frequency temporal modulations of speech in the spectrogram. For an end-to-end automatic speech recognition task, the FDLP spectrogram performs on par with the standard mel spectrogram features for clean read speech training and test data. For more realistic speech data with train-test domain mismatches or reverberations, FDLP spectrogram shows up to 25% and 22% relative WER improvements over mel spectrogram respectively.
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