Abstract
SpeechPy is an open source Python package that contains speech preprocessing techniques, speech features, and important post-processing operations. It provides most frequent used speech features including MFCCs and filterbank energies alongside with the log-energy of filter-banks. The aim of the package is to provide researchers with a simple tool for speech feature extraction and processing purposes in applications such as Automatic Speech Recognition and Speaker Verification.
Highlights
Automatic Speech Recognition (ASR) requires three main components for further analysis: Preprocessing, feature extraction, and post-processing
Feature extraction comes to our rescue for turning the high dimensional signal to a lower dimensional and yet a more informative version of that for sound recognition and classification (Furui 1986; Guyon et al 2008; Hirsch and Pearce 2000)
In ASR applications, the linguistic characteristics of the raw signal are of great importance and the other characteristics must be ignored
Summary
Automatic Speech Recognition (ASR) requires three main components for further analysis: Preprocessing, feature extraction, and post-processing. With the availability of free software for speech recognition such as VOICEBOX, most of these softwares are Matlab-based which limits their reproducibility due to commercial issues Another great package is PyAudioAnalysis (Giannakopoulos 2015), which is a the comprehensive package developed in Python. Considering the recent advent of deep learning in ASR and SR and the importance of the accurate speech feature extraction, here are the motivations behind SpeechPy package:. Developing a free open source package which covers important preprocessing techniques, speech features, and post-processing operations required for ASR and SR applications. SpeechPy has been developed to satisfy the aforementioned needs It contains the most important preprocessing and post-processing operations and a selection of frequently used speech features.
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