由模式识别和有限自动机组成的流水线用于语音功能亢进研究中的VCV产品识别

Gbenga Omotara, Mark L. Berardi, Maria Dietrich, G. DeSouza
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引用次数: 0

摘要

相对基频(RFF)是语音科学中用于量化声音力度的声学度量。由于它试图捕获稳态元音和不发音辅音之间的转换(即to/from),任何识别这些转换模式的机器学习方法都应该需要能够识别音素序列的时间属性。与此同时,神经网络(NN)已经成为数据驱动问题的普遍解决方案,递归神经网络(RNN)提供了一种时间序列模式来解决时间相关问题。实际上,典型的神经网络解决方案要么需要像RNN那样的时间序列模式,要么需要一些谱变换来处理与时间相关的数据。在本研究中,我们决定忽略(至少暂时忽略)数据的任何时间序列依赖性,并使用简单的神经网络对语音元素进行分类。随后,使用状态机来识别它们的序列,目的是定位元音-辅音-元音(VCV)产品中浊音和不浊音之间的转换。本研究的目的是证明由时间不可知(神经网络)和时间依赖(状态机)组件组成的管道可用于识别VCV产品中的时间依赖模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Pipeline Consisting of Pattern Recognition and Finite Automata for Recognizing VCV Productions in the Study of Vocal Hyperfunction
Relative fundamental frequency (RFF) is an acoustic measure used to quantify vocal effort in voice science. Since it seeks to capture transitions between (i.e. to/from) steady-state vowels and unvoiced consonants, any machine learning approach to recognize patterns in these transitions should require time properties capable of identifying the sequence of phonemes. At the same time, Neural Networks (NN) have become a ubiquitous solution for data-driven problems, and Recursive NNs (RNN) provide a time-series schema to address time-dependent problems. Indeed, typical Neural Network solutions require either a time-series schema like in RNN or some spectral transformation to be able to handle time-dependent data. In this study, we decided to ignore - at least momentarily - any time-series dependency of the data and employed a simple NN to classify elements of the speech. Later, a State-Machine was used to identify their sequence with the purpose of localizing the transitions between voiced and unvoiced sounds in vowel-consonant-vowel (VCV) productions. The goal of this study was to demonstrate that a pipeline consisting of time-agnostic (Neural Network) and time-dependent (State Machine) components can be used to recognize time-dependent patterns in VCV productions.
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