论文标题
感知性，非线性语音处理和尖峰神经网络
Perceptive, non-linear Speech Processing and Spiking Neural Networks
论文作者
论文摘要
在嘈杂和腐败的言语的背景下，来源的分离和语音识别非常困难。大多数传统技术都需要庞大的数据库来估算执行分离或识别的语音（或噪声）密度概率。我们讨论了感知性语音分析和处理与生物学上合理的神经网络处理器的潜力。我们说明了受听觉场景分析范式启发的源分离系统上语音的这种非线性处理的潜力。我们还讨论了语音识别的潜在应用。
Source separation and speech recognition are very difficult in the context of noisy and corrupted speech. Most conventional techniques need huge databases to estimate speech (or noise) density probabilities to perform separation or recognition. We discuss the potential of perceptive speech analysis and processing in combination with biologically plausible neural network processors. We illustrate the potential of such non-linear processing of speech on a source separation system inspired by an Auditory Scene Analysis paradigm. We also discuss a potential application in speech recognition.
