噪音識別 的英文怎麼說

中文拼音 [zàoyīnzhìbié]
噪音識別 英文
noise identification
  • 噪 : 動詞1. （蟲或鳥叫） chirp 2. （大聲叫嚷） make noise; make an uproar; clamour
  • 音 : 名詞1. （聲音） sound 2. （消息） news; tidings 3. [物理學] （音質） tone 4. （姓氏） a surname
  • 識 : 識Ⅰ動詞[書面語] （記） remember; commit to memory Ⅱ名詞1. [書面語] （記號） mark; sign 2. （姓氏） a surname
  • 別 : 別動詞[方言] （改變） change （sb. 's opinion）
  • 噪音 : noise; undesired sound; strepitus
  • 識別 : 1 （辯別; 辯認） discriminate; distinguish; discern; tell the difference; spot 2 [計算機] identif...
  1. Noisy chinese speech recognition based on linear prediction of one - sided autocorrelation sequence

    基於單邊自相關線性預測噪聲中漢語語音識別
  2. Speech enhancement as the front - end processing module is used to improve the signal - to - noise ratio ( snr ) of the input signal for recognition in the latter stages

    為了讓語音識別系統在安靜的環境和有噪聲的環境中都獲得令人滿意的工作性能，研究了一個將語音增強器和語音識別器級連起來的系統。
  3. Then this spectral subtraction method is applied to noise speech recognition system as the front - end processing. noise speech signal are processed to improve its snr before recognition. so the recognition rate can be improved in noise environments

    並將改進譜減演算法作為噪聲下語音識別系統的前端處理過程，即通過對含噪的語音進行語音增強以提高信號的信噪比，從而提高語音識別系統的抗噪聲性能。
  4. Speech recognition based on rasta - ff2 filters denoising technology

    2濾波降噪技術的語音識別
  5. The first part of the paper is designing the testing project for grounding resistance and insulation resistance in a new way. using 16bits ad converter with programmable control amplifier replaced the way which used changing resistance to change measure range. lt is not only improved testing precision and develop the system expediently, but also reduced the area of the circuit boardwith the new way. in order to make the electric implement safety testing system have upstanding expansibility, the software and hardware of the system adopted the modularization design. adopted mcu atmegal28 as a master mcu which control mmi, realtime clock and communication with slaver mcu. atemga8 as the slaver mcu to realize testing function. so it is easy to add or reduce the testing project. the testing implement system has been developed successfully, and the comments for the system is that it has high precision, high expansibility and easy maintain. but considering the electric implement system should have intelligence and humanity abi lity. so this paper bring forward a scheme of electric equipment safety testing embedded system with speech control. after introduce the basic theory of speech recognition, the paper expatiate the characters of this system. the system is a noise conditon, not special people, small glossary, insulation word system. with these characters design the speech recognition as fellow. utilizing cross zero ratio and short energy to ensure jumping - off point and end point ; adopting mfcc as the character parameters of speech recognition ; the character parameters than be recognized by dtw. in order to ensure the credibility of this project, first realized by matlab in computer

    在介紹了語音識別的基本原理后，闡述了本系統的特點：本系統是一個噪聲環境下非特定人、小詞匯量、孤立詞的語音識別系統。根據本系統的這些特點設計了如下語音識別方案：利用過零率和短時能量相結合的方式確定語音端點；採用mel頻率倒譜系數（ mfcc ）作為語音識別的特徵參數；得到的特徵參數最後通過動態時間規整（ dtw ）的模式識別方法進行識別。為了確保本系統實現方案的可靠性，首先通過計算機利用matlab軟體來模擬，在演算法模擬實現后又進一步增加環境的復雜性：加上較大的環境噪聲、突發性的噪聲等，再通過修改參數、修改參考模板、兩級識別等各種提高語音識別精度的方法來提廣東工業大學工學碩士學位論文高識別率。
  6. A number of techniques have been developed to reduce the mismatch caused by environment noise over the past decades

    然後基於此基本連續語音識別系統進行了抗噪聲技術的研究。
  7. In signal space, speech enhancement is adopted to effectively suppress the noise and increase the discriminative information embedded in noisy speech signal. however, the speech distortion introduced by enhancement, as well as the residual noise, is a very adverse factor for recognition

    在信號空間，利用語音增強有效抑制噪聲，提高輸入信號中的鑒別信息，但增強帶來的語音失真和增強后的剩餘噪聲是對語音識別非常不利的因素。
  8. A study on noisy speech recognition linear predictive coding prediction error

    一種噪聲環境下的語音識別方法線性預測誤差法的研究
  9. After the analysis of viterbi decoding, we conclude that the adverse effect of impulsive noise on recognition is that the impulsive noise introduces unreliable

    通過對viterbi譯碼過程的分析，得出沖激噪聲對語音識別的影響在於其引入了不可靠的概率差距。
  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

    它們是：線性預測誤差法，單邊自相關線性預測法，語音前端聲學處理法，正則相關分析的譜變換補償方法，特徵綜合法和同模極點增加法。實驗結果表明，這6種方法都有效地提高了噪聲環境中語音識別率，其中較好的方法在強噪聲環境中信噪比為0db的語音識別率達到80 %以上，為信噪比較低的噪聲環境中自動語音識別展現了美好前景。
  11. Speech recognition system based on schmm ann in noisy environment

    噪聲背景下的語音識別系統設計
  12. Study on noisy speech recognition methods

    噪聲環境下語音識別方法研究
  13. This paper presents a new method for speech recognition in stationary noise

    介紹一種平穩噪聲環境下語音識別的新的方法。
  14. Prevailing speech recognition systems can obtain a very high accuracy for clean speech recognition, but their performance will degrade rapidly in noisy environments owing to the mismatch between the acoustic models and the testing speech

    目前的語音識別系統對純凈語音可以達到非常高的識別精度，但是無處不在噪聲帶來了訓練模型和測試語音之間的失配，識別器的性能在噪聲環境中將會急劇下降。
  15. Because noise causes the mismatch between the acoustic models and the testing speech, the performance of speech recognition systems will degrade rapidly in noisy environments. therefore, noise robust technology is a very crucial problem for the speech recognition

    目前的連續語音識別系統對純凈語音已能達到非常高的識別精度，但是無處不在的背景噪聲帶來了訓練模型和測試語音之間的失配，這種失配使得連續語音識別系統的性能在噪聲環境中急劇下降。
  16. In the field of audio recognition, with many mature and creative technologies applying, especially the hidden markov models ( hmm ), the effect and efficient of the audio recognition system have been enhanced. but due to the mismatch between training and testing environment ( such as background, audio transition channel ), the recognition systems based on hmm tends to drastically degrade in performance

    在音頻信號識別特別是語音識別領域內，隨著隱馬爾科夫模型（ hmm ）的應用，使得系統的識別性能有了改進，但是由於訓練和測試環境（背景噪聲、音頻傳輸通道等）的失配常常導致識別性能的嚴重下降。
  17. One is using the autocorrelation function to detect the speech terminal, the other is using the coefficients based on the one - sided autocorrelation sequence to replace the original speech signal and then extract the speech feature to recognize. isolated word recognition experiment based on dtw shows : it can reduce the disturbance of noise effectively and get the high recognition rate. it is of great advantage to apply when snr signal to noise rate is low

    對含噪語音在自相關域上進行處理，以其自相關函數值為參數進行端點檢測，以基於單邊自相關序列的lpc倒譜系數作為語音的特徵參數進行語音識別，實驗表明：這種方法較好地消除了噪聲對語音信號的干擾，並獲得了較高的識別率。
  18. The noise robustness is one of the crucial factors that have deep influence upon the practicability of the speech recognition system, and then it has become the focus in the research field of automatic speech recognition

    語音識別系統的噪聲魯棒性是決定語音識別技術從實驗室走向實際應用的關鍵環節，是目前語音識別領域的研究熱點與難點。
  19. With the rapid growth of communication in wireless / computer networks, the research of robust speech recognition in impulsive noise environments has been a new hot topic

    隨著無線通信和計算機通信的迅速發展，對沖激噪聲下穩健語音識別技術的研究成為一個新的熱點。
  20. Autoregressive model - based robust speech recognition in additive noise environment

    基於自回歸模型的加性噪聲環境穩健語音識別
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