Noise Adaptation of Hmms Using Neural Networks
نویسندگان
چکیده
This paper proposes a new method, using neural networks, of adapting phone HMMs to noise added speech. The network is designed to map clean speech HMMs to noise-adapted HMMs using inputs of clean speech phone HMMs, noise HMMs and signal-to-noise ratios (S/N). The network is trained to minimize the mean squared error between the output HMMs and the target noise-adapted HMMs. Noisy broadcast-news speech was recognized in speaker-dependent and speaker-independent network training conditions, and the trained networks were confirmed to be effective in the recognition of new speakers and under new noise and S/N conditions.
منابع مشابه
Neural-network-based HMM adaptation for noisy speech
This paper proposes a new method, using neural networks, of adapting phone HMMs to noisy speech. The neural networks are designed to map clean speech HMMs to noise-adapted HMMs, using noise HMMs and signal-to-noise ratios (SNRs) as inputs, and are trained to minimize the mean square error between the output HMMs and the target noise-adapted HMMs. In evaluation, the proposed method was used to r...
متن کاملThe Application of Multi-Layer Artificial Neural Networks in Speckle Reduction (Methodology)
Optical Coherence Tomography (OCT) uses the spatial and temporal coherence properties of optical waves backscattered from a tissue sample to form an image. An inherent characteristic of coherent imaging is the presence of speckle noise. In this study we use a new ensemble framework which is a combination of several Multi-Layer Perceptron (MLP) neural networks to denoise OCT images. The noise is...
متن کاملAdaptation of deep neural network acoustic models using factorised i-vectors
The use of deep neural networks (DNNs) in a hybrid configuration is becoming increasingly popular and successful for speech recognition. One issue with these systems is how to efficiently adapt them to reflect an individual speaker or noise condition. Recently speaker i-vectors have been successfully used as an additional input feature for unsupervised speaker adaptation. In this work the use o...
متن کاملمعرفی شبکه های عصبی پیمانه ای عمیق با ساختار فضایی-زمانی دوگانه جهت بهبود بازشناسی گفتار پیوسته فارسی
In this article, growable deep modular neural networks for continuous speech recognition are introduced. These networks can be grown to implement the spatio-temporal information of the frame sequences at their input layer as well as their labels at the output layer at the same time. The trained neural network with such double spatio-temporal association structure can learn the phonetic sequence...
متن کاملAircraft Visual Identification by Neural Networks
In the present paper, an efficient method for three dimensional aircraft pattern recognition is introduced. In this method, a set of simple area based features extracted from silhouette of aerial vehicles are used to recognize an aircraft type from its optical or infrared images taken by a CCD camera or a FLIR sensor. These images can be taken from any direction and distance relative to the fly...
متن کامل