声学 absement 详解：量化语音数据时间序列表示之间的声学差异
Acoustic absement in detail: Quantifying acoustic differences across time-series representations of speech data
AI总结：
本文提出用 absement 量化语音时间序列间的声学差异，并通过基于动态时间规整的孤立词模板识别实验验证其有效性，取得 57.9% 的识别准确率，探讨了纯声学词识别模型的启示。
摘要：
语音信号是时间序列数据的典型范例。信号的声学特性会随时间变化，有时变化剧烈。然而，我们在语音学中最常进行的比较是瞬时声学测量之间的比较，例如共振峰值。在本文中，我讨论了 absement 这一概念，将其作为量化两个时间序列之间差异的方法。随后，我提供了一个将 absement 应用于人类和/或计算机语音识别语音分析的实验示例。该实验是一个基于模板的语音识别任务，使用动态时间规整来比较孤立词录音之间的声学特征。实验取得了 57.9% 的识别准确率。本文从将 absement 用作工具的角度讨论了实验结果，并探讨了在以词作为最小离散语言单位的情况下，使用纯声学模型进行口语词识别的意义。
英文摘要：
The speech signal is a consummate example of time-series data. The acoustics of the signal change over time, sometimes dramatically. Yet, the most common type of comparison we perform in phonetics is between instantaneous acoustic measurements, such as formant values. In the present paper, I discuss the concept of absement as a quantification of differences between two time-series. I then provide an experimental example of absement applied to phonetic analysis for human and/or computer speech recognition. The experiment is a template-based speech recognition task, using dynamic time warping to compare the acoustics between recordings of isolated words. A recognition accuracy of 57.9% was achieved. The results of the experiment are discussed in terms of using absement as a tool, as well as the implications of using acoustics-only models of spoken word recognition with the word as the smallest discrete linguistic unit.
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