• Title/Summary/Keyword: Noise robust speech recognition

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An Efficient Approach for Noise Robust Speech Recognition by Using the Deterministic Noise Model (결정적 잡음 모델을 이용한 효율적인 잡음음성 인식 접근 방법)

  • 정용주
    • The Journal of the Acoustical Society of Korea
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    • v.21 no.6
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    • pp.559-565
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    • 2002
  • In this paper, we proposed an efficient method that estimates the HMM (Hidden Marke Model) parameters of the noisy speech. In previous methods, noisy speech HMM parameters are usually obtained by analytical methods using the assumed noise statistics. However, as they assume some simplication in the methods, it is difficult to come closely to the real statistics for the noisy speech. Instead of using the simplication, we used some useful statistics from the clean speech HMMs and employed the deterministic noise model. We could find that the new scheme showed improved results with reduced computation cost.

A Study on the Robust Bimodal Speech-recognition System in Noisy Environments (잡음 환경에 강인한 이중모드 음성인식 시스템에 관한 연구)

  • 이철우;고인선;계영철
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.1
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    • pp.28-34
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    • 2003
  • Recent researches have been focusing on jointly using lip motions (i.e. visual speech) and speech for reliable speech recognitions in noisy environments. This paper also deals with the method of combining the result of the visual speech recognizer and that of the conventional speech recognizer through putting weights on each result: the paper proposes the method of determining proper weights for each result and, in particular, the weights are autonomously determined, depending on the amounts of noise in the speech and the image quality. Simulation results show that combining the audio and visual recognition by the proposed method provides the recognition performance of 84% even in severely noisy environments. It is also shown that in the presence of blur in images, the newly proposed weighting method, which takes the blur into account as well, yields better performance than the other methods.

Speech extraction based on AuxIVA with weighted source variance and noise dependence for robust speech recognition (강인 음성 인식을 위한 가중화된 음원 분산 및 잡음 의존성을 활용한 보조함수 독립 벡터 분석 기반 음성 추출)

  • Shin, Ui-Hyeop;Park, Hyung-Min
    • The Journal of the Acoustical Society of Korea
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    • v.41 no.3
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    • pp.326-334
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    • 2022
  • In this paper, we propose speech enhancement algorithm as a pre-processing for robust speech recognition in noisy environments. Auxiliary-function-based Independent Vector Analysis (AuxIVA) is performed with weighted covariance matrix using time-varying variances with scaling factor from target masks representing time-frequency contributions of target speech. The mask estimates can be obtained using Neural Network (NN) pre-trained for speech extraction or diffuseness using Coherence-to-Diffuse power Ratio (CDR) to find the direct sounds component of a target speech. In addition, outputs for omni-directional noise are closely chained by sharing the time-varying variances similarly to independent subspace analysis or IVA. The speech extraction method based on AuxIVA is also performed in Independent Low-Rank Matrix Analysis (ILRMA) framework by extending the Non-negative Matrix Factorization (NMF) for noise outputs to Non-negative Tensor Factorization (NTF) to maintain the inter-channel dependency in noise output channels. Experimental results on the CHiME-4 datasets demonstrate the effectiveness of the presented algorithms.

A Study on a Model Parameter Compensation Method for Noise-Robust Speech Recognition (잡음환경에서의 음성인식을 위한 모델 파라미터 변환 방식에 관한 연구)

  • Chang, Yuk-Hyeun;Chung, Yong-Joo;Park, Sung-Hyun;Un, Chong-Kwan
    • The Journal of the Acoustical Society of Korea
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    • v.16 no.5
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    • pp.112-121
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    • 1997
  • In this paper, we study a model parameter compensation method for noise-robust speech recognition. We study model parameter compensation on a sentence by sentence and no other informations are used. Parallel model combination(PMC), well known as a model parameter compensation algorithm, is implemented and used for a reference of performance comparision. We also propose a modified PMC method which tunes model parameter with an association factor that controls average variability of gaussian mixtures and variability of single gaussian mixture per state for more robust modeling. We obtain a re-estimation solution of environmental variables based on the expectation-maximization(EM) algorithm in the cepstral domain. To evaluate the performance of the model compensation methods, we perform experiments on speaker-independent isolated word recognition. Noise sources used are white gaussian and driving car noise. To get corrupted speech we added noise to clean speech at various signal-to-noise ratio(SNR). We use noise mean and variance modeled by 3 frame noise data. Experimental result of the VTS approach is superior to other methods. The scheme of the zero order VTS approach is similar to the modified PMC method in adapting mean vector only. But, the recognition rate of the Zero order VTS approach is higher than PMC and modified PMC method based on log-normal approximation.

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Weighted filter bank analysis and model adaptation for improving the recognition performance of partially corrupted speech (부분 손상된 음성의 인식성능 향상을 위한 가중 필터뱅크 분석 및 모델 적응)

  • Cho Hoon-Young;Oh Yung-Hwan
    • MALSORI
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    • no.44
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    • pp.157-169
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    • 2002
  • We propose a weighted filter bank analysis and model adaptation (WFBA-MA) scheme to improve the utilization of uncorrupted or less severely corrupted frequency regions for robust speech recognition. A weighted met frequency cepstral coefficient is obtained by weighting log filter bank energies with reliability coefficients and hidden Markov models are also modified to reflect the local reliabilities. Experimental results on TIDIGITS database corrupted by band-limited noises and car noise indicated that the proposed WFBA-MA scheme utilizes the uncorrupted speech information well, significantly improving recognition performance in comparison to multi-band speech recognition systems.

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Selective pole filtering based feature normalization for performance improvement of short utterance recognition in noisy environments (잡음 환경에서 짧은 발화 인식 성능 향상을 위한 선택적 극점 필터링 기반의 특징 정규화)

  • Choi, Bo Kyeong;Ban, Sung Min;Kim, Hyung Soon
    • Phonetics and Speech Sciences
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    • v.9 no.2
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    • pp.103-110
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    • 2017
  • The pole filtering concept has been successfully applied to cepstral feature normalization techniques for noise-robust speech recognition. In this paper, it is proposed to apply the pole filtering selectively only to the speech intervals, in order to further improve the recognition performance for short utterances in noisy environments. Experimental results on AURORA 2 task with clean-condition training show that the proposed selectively pole-filtered cepstral mean normalization (SPFCMN) and selectively pole-filtered cepstral mean and variance normalization (SPFCMVN) yield error rate reduction of 38.6% and 45.8%, respectively, compared to the baseline system.

Recognition Performance Improvement for Noisy-speech by Parallel Model Compensation Adaptation Using Frequency-variant added with ML (최대우도를 부가한 주파수 변이 PMC 방법의 잡음 음성 인식 성능개선)

  • Choi, Sook-Nam;Chung, Hyun-Yeol
    • Journal of Korea Multimedia Society
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    • v.16 no.8
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    • pp.905-913
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    • 2013
  • The Parallel Model Compensation Using Frequency-variant: FV-PMC for noise-robust speech recognition is a method to classify the noises, which are expected to be intermixed with input speech when recognized, into several groups of noises by setting average frequency variant as a threshold value; and to recognize the noises depending on the classified groups. This demonstrates the excellent performance considering noisy speech categorized as good using the standard threshold value. However, it also holds a problem to decrease the average speech recognition rate with regard to unclassified noisy speech, for it conducts the process of speech recognition, combined with noiseless model as in the existing PMC. To solve this problem, this paper suggests a enhanced method of recognition to prevent the unclassified through improving the extent of rating scales with use of maximum likelihood so that the noise groups, including input noisy speech, can be classified into more specific groups, which leads to improvement of the recognition rate. The findings from recognition experiments using Aurora 2.0 database showed the improved results compared with those from the method of the previous FV-PMC.

Robust Speech Recognition Using Missing Data Theory (손실 데이터 이론을 이용한 강인한 음성 인식)

  • 김락용;조훈영;오영환
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.3
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    • pp.56-62
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    • 2001
  • In this paper, we adopt a missing data theory to speech recognition. It can be used in order to maintain high performance of speech recognizer when the missing data occurs. In general, hidden Markov model (HMM) is used as a stochastic classifier for speech recognition task. Acoustic events are represented by continuous probability density function in continuous density HMM(CDHMM). The missing data theory has an advantage that can be easily applicable to this CDHMM. A marginalization method is used for processing missing data because it has small complexity and is easy to apply to automatic speech recognition (ASR). Also, a spectral subtraction is used for detecting missing data. If the difference between the energy of speech and that of background noise is below given threshold value, we determine that missing has occurred. We propose a new method that examines the reliability of detected missing data using voicing probability. The voicing probability is used to find voiced frames. It is used to process the missing data in voiced region that has more redundant information than consonants. The experimental results showed that our method improves performance than baseline system that uses spectral subtraction method only. In 452 words isolated word recognition experiment, the proposed method using the voicing probability reduced the average word error rate by 12% in a typical noise situation.

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Robust Speech Recognition using Noise Compensation Method Based on Eigen - Environment (Eigen - Environment 잡음 보상 방법을 이용한 강인한 음성인식)

  • Song Hwa Jeon;Kim Hyung Soon
    • MALSORI
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    • no.52
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    • pp.145-160
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    • 2004
  • In this paper, a new noise compensation method based on the eigenvoice framework in feature space is proposed to reduce the mismatch between training and testing environments. The difference between clean and noisy environments is represented by the linear combination of K eigenvectors that represent the variation among environments. In the proposed method, the performance improvement of speech recognition systems is largely affected by how to construct the noisy models and the bias vector set. In this paper, two methods, the one based on MAP adaptation method and the other using stereo DB, are proposed to construct the noisy models. In experiments using Aurora 2 DB, we obtained 44.86% relative improvement with eigen-environment method in comparison with baseline system. Especially, in clean condition training mode, our proposed method yielded 66.74% relative improvement, which is better performance than several methods previously proposed in Aurora project.

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Cepstrum PDF Normalization Method for Speech Recognition in Noise Environment (잡음환경에서의 음성인식을 위한 켑스트럼의 확률분포 정규화 기법)

  • Suk Yong Ho;Lee Hwang-Soo;Choi Seung Ho
    • The Journal of the Acoustical Society of Korea
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    • v.24 no.4
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    • pp.224-229
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    • 2005
  • In this paper, we Propose a novel cepstrum normalization method which normalizes the probability density function (pdf) of cepstrum for robust speech recognition in additive noise environments. While the conventional methods normalize the first- and/or second-order statistics such as the mean and/or variance of the cepstrum. the proposed method fully normalizes the statistics of cepstrum by making the pdfs of clean and noisy cepstrum identical to each other For the target Pdf, the generalized Gaussian distribution is selected to consider various densities. In recognition phase, we devise a table lookup method to save computational costs. From the speaker-independent isolated-word recognition experiments, we show that the Proposed method gives improved Performance compared with that of the conventional methods, especially in heavy noise environments.