用類神經網路模型模擬語音感知的神經機制

Simulation of Neural Mechanism for Speech Perception with Neural Network Model

李明鴻(Ming-hong Li)

指導教授 ： 吳炤民

國立中央大學/資訊電機學院/電機工程學系/碩士(2011年)

摘要

經由心理語言學實驗的結果得知，“感知磁吸效應” (perceptual magnet effect)是種影響到幼兒往後語言發展的重要因素之一，這種效應會造成聽覺感知空間受到扭曲，導致一個音位(phoneme)周遭的聲音都會被歸成同一類範疇。本研究的目的是以類神經網路發展一種能模擬語音感知(speech perception)的模型，以類神經網路的非監督式學習(unsupervised learning)方式讓模型能從語音上的共振峰中找出一個音位的語音範疇(phonetic category)，來模擬人類從聽覺上獲得語言的過程。本論文透過修改自我組織映射(Self-Organizing Map，SOM)演算法以及藉由心理語言學實驗結果比較，讓模型能呈現英文母音的聽覺感知空間。從模擬結果顯示模型能辨認英文子音/r/與/l/、典型音與非典型音的差異以及形成母音的聽覺感知空間。而且本論文透過模擬語音感知及結合具有語音產生能力的類神經網路模型(Directions Into Velocities Articulator, DIVA)，呈現人類獲得言語能力的過程，例如讓模型去學習產生英文或中文母音等等。目前除了能讓DIVA 模型學習英文母音以外，更進一步的推廣至中文母音的發音(/ㄚ/、/一/、/ㄨ/、/ㄝ/、/ㄛ/、/ㄩ/)。未來將繼續發展本論文的模型，希望能用於探討大腦與語言之間的關係，藉此衍生至臨床上的應用。

關鍵字

自我組織映射 ； 聽覺感知 ； 語音感知 ； 類神經網路 ； 感知磁吸效應

並列摘要

Based on the results of the psycholinguistic experiments, the perceptual magnet effect is the important factor in speech development. This effect produced a warped auditory space to the corresponding phoneme. The purpose of this study was to develop a neural network model in simulation of speech perception. The neural network model with unsupervised learning was used to determine the phonetic categories of phoneme according to the formant frequencies of the vowels. The modified “Self-Organizing Map”(SOM) algorithm was proposed to produce the auditory perceptual space of English vowels. Simulated results were compared with findings from psycholinguistic experiments, such as categorization of English /r/ and /l/ and prototype and non-prototype vowels, to indicate the model’s ability to produce auditory perception space. In addition, this speech perception model was combined with the neural network model (Directions Into Velocities Articulator, DIVA) to simulate categorization of ten English vowels and their productions to show the learning capability of speech perception and production. We further extended this modified DIVA model to show its capability to categorize six Chinese vowels (/a/、/i/、/u/、/e/、/o/、/y/) and their productions. Finally, this study proposed further development and related discussions for this speech perception model and its clinical application.

並列關鍵字

Self-Organizing Map ； perceptual magnet effect ； neural network ； auditory perception ； speech perception

參考文獻

[1] Benzeghiba, M., Mori, R. D., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., et al. (2007). Automatic speech recognition and speech variability: A review. Speech Communication , 47, pp. 763-786.

[2] Buchsbaum, B. R., Hickok, G., and Humphries, C. (2001). Role of left posterior superior temporal gyrus in phonological processing for speech perception and production. Cognitive Science , 25, pp.663-678.

[3] Damper, R. I. and Harnad, S. R. (2000). Neural network models of categorical perception. Perception & Psychophysics , 62, pp. 843-867.

[4] Du, K.-L. (2010). Clustering: A neural network approach. Neural Networks , 23, pp. 89-107.

[6] Garcia, D., Hall, D. A., and Plack, C. J. (2010). The effect of stimulus context on pitch representations in the human auditory cortex. NeuroImage , 51, pp. 808–816.