| 1. | Asr automatic speech recognition
自动语音识别 |
| 2. | Automatic speech recognition
自动语音识别 |
| 3. | 345 introduces students to the rapidly developing field of automatic speech recognition
345向学生介绍自动语音识别这一快速发展中的领域。 |
| 4. | In the recent years large progress has been made in speech auto - recognition ; many voice speech systems have been developed
近年来，自动语音识别研究取得了突破性的进展，出现了许多不同类型的语音识别系统。 |
| 5. | The framework and functions of the system based on commercial automatic speech recognition ( asr ) engine are introduced
摘要介绍实现商用自动语音识别的系统架构及其功能，阐述应用自动语音识别技术实现的新通信增值业务。 |
| 6. | Automatic speech recognition is used more and more widely in people ’ s life , which is categorized into continuous speech recognition and keyword spotting
自动语音识别技术在当代人们的生活中有了越来越广泛的应用。目前自动语音识别又大致分为连续语音识别和关键词识别。 |
| 7. | Keyword spotting is also a good solution for problems of tongue , such as non - standard , incoherence , etc . when there are many differences between the speeches for training and the speeches for testing , the performance of the system is greatly degraded
这对于在自然对话情景下口语的不规范、不连贯等问题也是一种很好的解决方案。在自动语音识别中,当训练语音和识别语音有较大差别时,将导致系统的识别率急剧下降。 |
| 8. | The application of artificial neural networks ( ann ) to automatic speech recognition ( asr ) is investigated in this thesis . the recognition of speaker - independent and isolated words is focused and three types of ann model are presented . the related algorithms and programs are developed
本文基于自动语音识别( asr )的原理和过程，结合人工神经网络( ann )的建模理论及特点，主要研究了人工神经网络在自动语音识别中的应用问题。 |
| 9. | Based on the theory and procedure of the automatic speech recognition ( asr ) ， and combined the theory and characteristics of the artificial neural networks ( ann ) ， the research in this paper is oriented on the theory and application of the mixed model ? hmmnn ， which is formed by the combination of the hidden markov model ( hmm ) and the self - organized neural networks （ sonn ） ， and the related algorithms and model are developed
本文基于自动语音识别（ asr ）的原理和过程，结合人工神经网络( ann )的建模理论及特点，主要研究了隐含马尔可夫模型（ hmm ）与自组织神经网络( sonn )相结合的混合模型hmmnn的原理及在语音识别中的应用，分析构造了相应的语音识别模型与算法。 |
| 10. | There are difficulties in noisy speech recognition , especially low signal - to - noise rations are more difficult . this paper describes briefly six methods for speaker - dependent noisy speech recognition isolated words . they are lpc prediction error method , one - side auto - correlation sequence lpc , acoustic front end processing , canonical correlation based on compensation method , combination of features method and increase of poles method . the experimental results show that all the six techniques can improve effectively noisy speech recognition , and the best noisy speech recognition rate is above 80 % when snr 0db
它们是：线性预测误差法，单边自相关线性预测法，语音前端声学处理法，正则相关分析的谱变换补偿方法，特征综合法和同模极点增加法。实验结果表明，这6种方法都有效地提高了噪声环境中语音识别率，其中较好的方法在强噪声环境中信噪比为0db的语音识别率达到80 %以上，为信噪比较低的噪声环境中自动语音识别展现了美好前景。 |
