星空的尽头是我的家园,我要回家 《家园》

我们发现自己只是一名匆匆的过客，我们发现自己的祖先住在遥远地天边，我们将抛弃眼前的一切，为了回到我们自己的家园。哪怕流尽鲜血，哪怕丧失生命，也要把笑容镌刻在自己的土地上。     

语音识别技术架构下的英语音标辅助学习平台开发及应用研究

针对传统英语发音学习过程中存在的发音不准确、缺乏发音评价和纠错指导的现状。提出了基于Android平台开发一款发音跟读、发音比对、发音评分和纠错多功能应用的英语发音训练系统。基于短时过零率端点检测进行语音预处理，获得较为稳定的语音段信号，利用美尔倒普系数（MFCC）提取语音信号特征值，获得每帧语音频谱特性。通过矢量间距离计算表征信号的匹配度，在自适应（AP）评价法来实现平均匹配距离与发音评分间的逻辑关系，得到发音共振峰包络图，利用生成的发音共振峰对比图构建发音共振峰和读音口型模型进行发音跟读质量反馈。实际应用结果表明：开发的系统应用能够准确进行定性化的发音口型纠正，有效满足现代英语学习过程中的智能化、实时性和便携化需求。     

探析小学音乐合唱教学对学生歌唱技巧的训练

在音乐课程中,有许多的唱歌技巧,合唱教学是其中的一种,对学生的音乐学习有很大的帮助,音乐教师在课程教学中,应该更加重视合唱教学,指导学生合唱训练。了解到处于这个阶段学生的发音特征,为学生选择合适的歌唱技巧,不断帮助学生提高音乐水平,产生对音乐的兴趣,推动着音乐教学的发展。     

士兵突击

我想我自己从来没有找到答案，发现自己在一个圆里拼命跑圈； 结果不过是一次次重复的循环，我想要突围想要打破这个怪圈。     

一种基于小波和快速傅里叶变换的学习型歌唱系统

语音识别和合成技术分别实现了计算机理解人类语言和模仿人类阅读文本的功能,提出了一种实现计算机学习并演唱歌曲的系统。系统运用敲击定位法定位发音时刻,然后利用Daubechies小波变换和快速傅里叶变换计算出对应的基频,采用语音合成技术输出声音。     

学会做最好的自己

很多时候，我们周围的群体讨论的都是如何学习、追赶并超越标杆的问题，这也自然出现了当一个先进出现时，无数的模仿者会前赴后继，有模仿模式，也有模仿细节，但无一例外的却发现，原来成功很多时候无法复制，最终只能落个画龙画虎难画骨的局面。而近日，一次偶然的采访中，有集团公司高层表示业务拓展的最核心观点就是做好自己。在他看来，所谓的成功商业模式、创新一定程度上是难以复制的。因此，脚踏实地，做好自己或许是企业发展最实际、最正确的道路之一。     

基于SOM网络的语音训练可视化方法研究

提出了一种利用SOM网络输出层可视化的特点进行语音训练的方法。SOM网络能够将输入向量映射到二维平面或曲面上,受试者通过视觉反馈的位置信息,指导其发音行为。为了提高SOM聚类效果,SOM还进行加强训练;讨论了SOM输出层神经元个数对聚类的影响。实验结果表明,提出的利用SOM语音训练方法,直观简单,能够有效地实现“看图说话”。     

错音检测及其在语音教学中的应用综述

在学习语音的过程中,找出学习者发音的错误并加以改进是非常重要的。错音检测技术就是自动诊断语流中错误发音的技术,也是计算机辅助发音训练研究的主要内容之一。该文总结了错音检测技术的研究和应用现状,分别介绍了基于语音识别、基于错音网络和基于声学语音学的错音检测技术。在此基础上又介绍了错音检测技术在计算机辅助发音训练系统中的应用,以及汉语自动发音评估技术的发展。文章最后给出了作者的分析和建议。     

浅谈小学语文朗读教学

让学生喜欢上语文,加强朗读训练是一种行之有效的好手段。现就小学语文朗读指导所容易忽视的问题提出自己的一些愚见:一是要让语文课堂充满朗朗的读书声。二是老师们可以从以下五个方面进行指导:跟读模仿,激发兴趣;句读停顿,读通读畅;轻声重音,突出关键;读之而喜,读之而悲;朗读默读,相得益彰。     

“微”高考

@篱笆某考生在高考之前梦见自己在考场，可见其压力之大，但“杯具”的是当他醒来的时候发现自己真的在考场。     

Multilingual recognition of non-native speech using acoustic model transformation and pronunciation modeling

This article presents an approach for the automatic recognition of non-native speech. Some non-native speakers tend to pronounce phonemes as they would in their native language. Model adaptation can improve the recognition rate for non-native speakers, but has difficulties dealing with pronunciation errors like phoneme insertions or substitutions. For these pronunciation mismatches, pronunciation modeling can make the recognition system more robust. Our approach is based on acoustic model transformation and pronunciation modeling for multiple non-native accents. For acoustic model transformation, two approaches are evaluated: MAP and model re-estimation. For pronunciation modeling, confusion rules (alternate pronunciations) are automatically extracted from a small non-native speech corpus. This paper presents a novel approach to introduce confusion rules in the recognition system which are automatically learned through pronunciation modelling. The modified HMM of a foreign spoken language phoneme includes its canonical pronunciation along with all the alternate non-native pronunciations, so that spoken language phonemes pronounced correctly by a non-native speaker could be recognized. We evaluate our approaches on the European project HIWIRE non-native corpus which contains English sentences pronounced by French, Italian, Greek and Spanish speakers. Two cases are studied: the native language of the test speaker is either known or unknown. Our approach gives better recognition results than the classical acoustic adaptation of HMM when the foreign origin of the speaker is known. We obtain 22% WER reduction compared to the reference system.     

基于可视语音的英语发音辅导系统

发音问题是初学英语的一大难题。在我国这样的非英语环境中,很多小学生课后缺少专业老师辅导,极易出现英语发音障碍。本文设计开发了一个基于可视语音的英语发音辅导系统EP Tutor,模拟一个卡通家教的脸部动画,生动亲切的为学生一对一辅导英语发音。本文重点讨论了系统设计理念、系统架构、部分关键功能的详细设计以及关键技术的实现。     

Research on automatic evaluation method of Mandarin Chinese pronunciation based on 5G network and FPGA

In the automatic evaluation system, need to learn the standard mandarin of scoring method for teaching in native Chinese pronunciation. The most pronounced goal protocols focus on the context in which native speakers are unnatural. The new Hidden Markov Model (HMM) algorithm based on the traditional algorithm likely algorithm for Chinese syllables, whose final initial period is found in the area where evidence for the measurement of weight control has been found. Experiments have also shown that this algorithm is more effective than the traditional posterior recording algorithm of the Mandarin learning method. Force Hidden Markov Model- HMM Align alignment identification for each syllable and associated recording probability for speech evaluation via race-based reliability system applications. These processes could then be formalized as a linear combination after the overall assessment functions: phonics, tone, intensity, and rhythm. Because both linear and non-linear parameters are involved in the overall evaluation functions. Incorporates variation in pronunciation to generate structure through a novel approach that incorporates tons of sub-tones that represent the missing automatic sound models. The word level assessment achieved through the pronunciation is similar to that which in the future showed the singing ability being realized by the evaluation system in full-length pronunciation as a method.     

母语与非母语语音识别声学建模

为了兼容母语与非母语说话人之间的发音变化,提出一种新的声学模型建模方法。分析中国人受母语影响产生的英语发音变化,利用中国人英语发音数据库自适应得到语音模型,采用声学模型融合技术构建融合2种发音规律的识别模型。实验结果证明,中国人英语发音的语音识别率提高了13.4%,但标准英语的语音识别率仅下降1.1%。     

双语者加工汉语母语语义时对英语的ERP激活效应的研究

本研究采用ERP实验,以被试的反应时间、错误率和脑电成分N400为参考因素,探索高级双语者在加工第一语言时是否自动检索第二语言。结果显示,内隐的英语首发音条件引起的效应没有体现在反应时间上。在ERP实验结果中,被试在判断语义相关的词语时,大脑语言区域的N400在词语英译首发音一致与否的情况下差异不显著;而判断语义无关的词语时,N400在该条件下显著。实验结果分析表明,高级双语者在深度加工第一语言时,大脑可能无意识地检索第二语言。     

语音词汇网单词联想记忆

语音联想记忆是一种高效的记忆方法。为了给学习者提供语音联想的素材,引导学习者进行语音联想,熟悉读音规则,加深对单词拼写和发音的记忆,帮助学习者建立字母组合与相关发音间的双向认知,提出一种基于语音的词汇网。语音词汇网是基于常见字母组合和单词读音之间的差异构建的,因此其中既包含了语音近似度信息,也包含了一定的单词结构信息。利用该网络,学习系统不但可以实现语音联想功能,还能提供语音方面的相关统计信息。语音词汇网的引入能进一步完善单词学习系统的联想记忆功能。     

基于praat软件的俄语读音分析

用语音合成与分析软件praat分析了中国学生俄语读音的一些特点。通过对语音信号的波形图、语图谱、基音、共振峰、音高、音强等声学特性的分析,研究了中国学生在音素、音节、重音、音调、节奏、语调等方面存在的差异,为纠正其不良的发音、读句习惯提供技术帮助。     

Automatic pronunciation scoring of words and sentences independent from the non-native’s first language

This paper describes an approach for automatic scoring of pronunciation quality for non-native speech. It is applicable regardless of the foreign language student’s mother tongue. Sentences and words are considered as scoring units. Additionally, mispronunciation and phoneme confusion statistics for the target language phoneme set are derived from human annotations and word level scoring results using a Markov chain model of mispronunciation detection. The proposed methods can be employed for building a part of the scoring module of a system for computer assisted pronunciation training (CAPT). Methods from pattern and speech recognition are applied to develop appropriate feature sets for sentence and word level scoring. Besides features well-known from and approved in previous research, e.g. phoneme accuracy, posterior score, duration score and recognition accuracy, new features such as high-level phoneme confidence measures are identified. The proposed method is evaluated with native English speech, non-native English speech from German, French, Japanese, Indonesian and Chinese adults and non-native speech from German school children. The speech data are annotated with tags for mispronounced words and sentence level ratings by native English teachers. Experimental results show, that the reliability of automatic sentence level scoring by the system is almost as high as the average human evaluator. Furthermore, a good performance for detecting mispronounced words is achieved. In a validation experiment, it could also be verified, that the system gives the highest pronunciation quality scores to 90% of native speakers’ utterances. Automatic error diagnosis based on a automatically derived phoneme mispronunciation statistic showed reasonable results for five non-native speaker groups. The statistics can be exploited in order to provide the non-native feedback on mispronounced phonemes.     

Confidence measures from local posterior probability estimates

In this paper we introduce a set of related confidence measures for large vocabulary continuous speech recognition (LVCSR) based on local phone posterior probability estimates output by an acceptor HMM acoustic model. In addition to their computational efficiency, these confidence measures are attractive as they may be applied at the state-, phone-, word- or utterance-levels, potentially enabling discrimination between different causes of low confidence recognizer output, such as unclear acoustics or mismatched pronunciation models. We have evaluated these confidence measures for utterance verification using a number of different metrics. Experiments reveal several trends in “profitability of rejection", as measured by the unconditional error rate of a hypothesis test. These trends suggest that crude pronunciation models can mask the relatively subtle reductions in confidence caused by out-of-vocabulary (OOV) words and disfluencies, but not the gross model mismatches elicited by non-speech sounds. The observation that a purely acoustic confidence measure can provide improved performance over a measure based upon both acoustic and language model information for data drawn from the Broadcast News corpus, but not for data drawn from the North American Business News corpus suggests that the quality of model fit offered by a trigram language model is reduced for Broadcast News data. We also argue that acoustic confidence measures may be used to inform the search for improved pronunciation models.