語音特徵 的英文怎麼說

中文拼音 [yǔyīntèzhǐ]
語音特徵 英文
phonetic feature
  • 語 : 語動詞[書面語] （告訴） tell; inform
  • 音 : 名詞1. （聲音） sound 2. （消息） news; tidings 3. [物理學] （音質） tone 4. （姓氏） a surname
  • 特 : Ⅰ形容詞（特殊; 超出一般） particular; special; exceptional; unusual Ⅱ副詞1 （特別） especially; v...
  • 徵 : 名詞[音樂] （古代五音之一 相當于簡譜的「5」） a note of the ancient chinese five tone scale corre...
  • 語音 : speech sounds; pronunciation; voice
  • 特徵 : characteristic; feature; properties; aspect; trait
  1. The different which can represent a phoneme in different phonetic environments are called the allophones of that phoneme

    音位是音系學研究的單位，是抽象的概念，每一個音位是一組語音特徵的集合體，音位具有區別意義的作用。
  2. Moreover in speech enhancement, especially in reducing the pulse noise, morphological algorithm has its unique advantage. particularly morphological filter may maintain the preferable accurate of the speech signal in speech waveform, and which produces little impairment to the formant of speech. so the spectrum structure of the speech is retained well, and the quality of the speech will not be reduced

    特別是，在時域波形分析中，形態學濾波增強較小波去噪更好地保持語音信號的細節；在頻域分析中，形態學濾波對語音信號的基音頻率、頻譜斜率、共振峰等語音特徵的影響很小，因而能夠較好的保留語音信號的頻譜結構，使語音品質不致降低。
  3. The semantic characteristic of monosyllable verbs and its ' terms ' confirmation in quot; discussions about salt and iron quot

    單音節動詞的語義特徵及其詞項的確定
  4. Fast wavelet analysis algorithm based on oblique projection and mellin transform

    基於小波包變換的說話人語音特徵參數的提取
  5. The statistic of wavelet transform coefficient algorithm can solve the periodic noise, high - energy noise and some non - gauss noise simply and effectively ; bi - spectrum can acquire more information from the original signal than power - spectrum, detect more information except from range and restrain the gauss noise. short - time speech signal can be considered as stationary and with periodic non - gauss signal, so we can make use of bi - spectrum to obtain the speech character and separate the speech and noise and detect morse telegraph signal ; complex number spectrum variance algorithm is put forward based on the deeply observing speech data, it is a new algorithm, experiment show that it is simple, effective

    統計演算法在解決周期信號、高能噪聲和高斯信號方面有獨特之處，能簡單有效提取以上噪聲的特徵;雙譜能夠提供比功率譜更多的有用信息，有效地檢測信號幅度之外的其它信息，並能有效抑制高斯噪聲，短時語音信號一般認為是平穩且有一定的周期性的非高斯信號，因而可以利用雙譜來提取語音信號特性並實現信噪分離;復數譜方差演算法是在對語音信號進行深入觀察和分析的基礎上而提出來的一種全新的語音特徵提取方法，此方法簡單而有效的提取了語音、噪聲的特徵以及檢測莫爾斯信號，基於實驗表明，該演算法取得了很好的效果。
  6. The present paper makes an analysis of the character, the function, the cause and the phonetic characteristics of light tones

    摘要對輕聲的性質、輕聲的功能、輕聲的原因及輕聲的語音特徵進行了分析。
  7. In this thesis, first we analyzed and designed a traditional continued speech recognition system, which based on hmm and mfcc speech features. then we researched some noise robust technologies based on that system

    本論文首先分析並實現了一個以mel頻率倒譜系數（ mfcc ）作為語音特徵，基於隱馬爾可夫模型（ hmm ） ，針對連續數字串識別任務的基本連續語音識別系統。
  8. An analysis of song qing - lings speech recording can prove her accent of chuansha and old shanghai counties

    摘要通過對宋慶齡的講話錄音進行語音分析，可以說明在她的語音中有川沙語音和上海縣城老派語音特徵。
  9. After making a full thought on the previous computer phonetic processing and recognition technology, the author devotes the paper to the designation of phonetic samples, phonetic boundary detection, phonetic auto - segmentation, the design of phonetic distinctive feature database, the memory ways for store phonetic data, the contrastive contents about words and phrases on psc examine papers and the design of the system window. the author also offers a typical flow diagram and a program about using the visual studio software to draw procedures which plays an important role on the realization about the final success of the software system discussed on this paper

    在借鑒和參考目前計算機語音處理和識別技術的基礎上，著重對語音樣本的選取、語音端點檢測、語音自動分段、以及語音特徵數據庫的設計、語音數據的存儲方式、 psc試卷單字、詞語的對比內容、系統的界面設計進行了深入的探討和研究，詳細列出了一些有代表性的流程圖，提出了利用visualstudio可視化計算機軟體進行編程的具體方案，對本軟體的最終程序實現具有指導作用。
  10. A serial generalized morphological filter with multi - structural element is used suppression white gaussian noise or pulse noise embedded in the speech signal. the paper compares morphological speech enhancement algorithm with classical approach on the feature of speech in the frequency domain and time domain

    本文針對形態學在數字語音信號增強中的應用演算法研究，採用多結構元素的廣義形態濾波器，主要用於對被高斯白噪聲或正負脈沖噪聲污染的語音信號的濾波增強，深入研究形態學濾波的語音增強演算法在語音時域、頻域對語音特徵參數的影響。
  11. Linguistic elements, such as phonological features, syntactic units, and language groups are related to each other in complex ways. these relationships can be represented by matrices, networks, trees, and other graphic devices

    語音特徵、句法單位、語言分支等語言元素之間的關系異常復雜，可用矩陣、網路、樹和其他圖像方式表示。
  12. Feature extraction based on wavelet transformation in speaker recognition

    基於小波變換的說話人語音特徵參數提取
  13. A robust feature - extraction method based on wavelet transform for text - independent speaker identification

    變換的非特定人語音特徵提取方法
  14. Fourthly, since the missing between training and practical environments is the fundamental reason for the degradation of performance of automatic speech recognition, we have proposed a method to compensate and amend hmm to adapt noise environments. experiments show that better noisy robustness can be achieved, especially in stationary background noisy environments

    在語音特徵參數級去噪的基礎上，提出了一種基於hmm和倒譜特徵的噪聲補償方法，通過對純凈環境下的模型參數的補償與修正，實現訓練環境與測試環境的匹配。
  15. Based on that, two key questions are proposed : one of them is constructing the visual speech representation model, the other, audio / visual mapping model

    在基於對可視語音合成問題分析的基礎上，提出了可視語音合成系統研究方法中首先要解決的2個問題：視覺語音特徵模型的構建和聲視頻映射模型的構建。
  16. By using the lpc coefficients of noise to predict all the speech signal, the method gets the lpc prediction error lpcpe sequence. then use it to substitute the speech sequence to detect the speech terminal extract the speech features and to recognize in a suitable way

    該方法利用噪聲的lpc系數去預測語音信號，從而得到lpc預測序列，然後把它代替原語音序列來進行語音端點的檢測語音特徵的提取和在合適的匹配方式下的識別。
  17. Back - up and restore your speech profile

    備份和恢復您的語音特徵資料
  18. A comparative research on chinese children ' s implicit and explicit learning of english phonological traits

    漢語兒童英語語音特徵內隱與外顯學習的比較研究
  19. They include : 1 ) speech enhancement, 2 ) extracting robust speech features, 3 ) speech model compensation for noisy environments, 4 ) missing feature

    目前的抗噪聲技術主要分為四類:語音增強法、提取抗噪語音特徵法、噪聲補償法、丟特徵法。
  20. ( 4 ) in the thesis, the transformation of speech signals of different speaker is completed by bp neural network. the transformation of single word is completed

    （ 4 ）本文利用bp神經網路來實現不同說話人語音特徵的轉換，基本上實現了單個詞的語音特徵的轉換。
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