用卷积变分自编码器压缩语音谱图，提升自动语音识别性能。
Convolutional Variational Autoencoders for Spectrogram Compression in Automatic Speech Recognition
- 用卷积VAE将语音谱图压缩为13维嵌入向量。
- 在300毫秒嵌入下，生成特征使ASR系统性能接近MFCC。
- 适合需要高效语音特征表示的研究者或工程师。
在许多自动语音识别（ASR）任务中，频谱图比梅尔频率倒谱系数（MFCC）表现更优，但因其特征空间维度复杂而难以使用。本文提出一种基于卷积变分自编码器（Convolutional VAE）的压缩频谱图表示方法。模型在LibriSpeech数据集子集上训练，从13维嵌入重建25毫秒的短片段频谱图。在Google Speech Commands数据集的语音命令语料库上，使用40维（300毫秒）嵌入生成特征，并构建了对应的ASR系统，与基于MFCC的模型进行对比。
原文摘要 · Abstract (English)
For many Automatic Speech Recognition (ASR) tasks audio features as spectrograms show better results than Mel-frequency Cepstral Coefficients (MFCC), but in practice they are hard to use due to a complex dimensionality of a feature space. The following paper presents an alternative approach towards generating compressed spectrogram representation, based on Convolutional Variational Autoencoders (VAE). A Convolutional VAE model was trained on a subsample of the LibriSpeech dataset to reconstruct short fragments of audio spectrograms (25 ms) from a 13-dimensional embedding. The trained model for a 40-dimensional (300 ms) embedding was used to generate features for corpus of spoken commands on the GoogleSpeechCommands dataset. Using the generated features an ASR system was built and compared to the model with MFCC features.
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