## Uncertainty decoding for noise robust speech recognition (2004)

Venue: | in Proc. Interspeech |

Citations: | 36 - 12 self |

### BibTeX

@TECHREPORT{Liao04uncertaintydecoding,

author = {Hank Liao},

title = {Uncertainty decoding for noise robust speech recognition},

institution = {in Proc. Interspeech},

year = {2004}

}

### Years of Citing Articles

### OpenURL

### Abstract

This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration. It has not been submitted in whole or in part for a degree at any other university. Some of the work has been published previously in conference proceedings

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Citation Context ...e following sections. The first two may be classified as adaptation techniques, the second two normalisation schemes. 2.5.1 Maximum Likelihood Linear Regression ML linear regression (MLLR) adaptation =-=[46, 89]-=- estimates an affine transformation of the acoustic model parameters. The transformation maximises the likelihood of the available adaptation data. Since the amount of adaptation data is usually limit... |

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Citation Context ...o share training data. Which states are tied together may be determined using data-driven clustering, however unseen contexts cannot be clustered. Alternatively, state clustering using decision trees =-=[7, 111, 154]-=- built from expert phonetic knowledge avoids this issue. An example decision tree is shown in figure 2.8.sCHAPTER 2. HIDDEN MARKOV MODEL SPEECH RECOGNITION 16 Figure 2.8: Decision tree for triphone st... |

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Citation Context ...stems described. A more complex system would typically use some form of feature projection scheme such as HLDA [87] or fMPE [116], advanced covariance modelling such as STC [41], and MMI [141] or MPE =-=[115]-=- training of model parameters—the use of such techniques were not investigated in these experiments.sCHAPTER 9. EXPERIMENTAL RESULTS ON RECORDED NOISY SPEECH 127 9.1.1 Predictive Model Compensation Ta... |

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Citation Context ... simplified by combining the various additive and convolutional noise sources into single additive noise, z(τ), and linear channel noise, h(τ), variables. Doing so gives this standard, oft-used model =-=[1, 39, 106]-=- of the noisy acoustic environment in the time domain show in figure 3.2. The noisy signal is now given by y(τ) = x(τ) ∗ h(τ) + z(τ) (3.2) where y(τ) is the noise corrupted speech and x(τ) the “clean”... |

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Citation Context ...o share training data. Which states are tied together may be determined using data-driven clustering, however unseen contexts cannot be clustered. Alternatively, state clustering using decision trees =-=[7, 111, 154]-=- built from expert phonetic knowledge avoids this issue. An example decision tree is shown in figure 2.8.sCHAPTER 2. HIDDEN MARKOV MODEL SPEECH RECOGNITION 16 Figure 2.8: Decision tree for triphone st... |

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Citation Context ...onents can remain unchanged, however anywhere from 25-1000 observations need to be generated per Gaussian in the system [47]. DPMC gave results equivalent to matched systems at levels below 20 dB SNR =-=[52]-=-. However, this iterative estimation is computationally expensive. 4.4.3 Vector Taylor Series Model Compensation As the discussion of PMC shows, deriving a corrupted speech output distribution, given ... |

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Citation Context ...ired and the noise condition may vary. Artificial methods of corrupting the training data have been explored which also yield good results. Samples of noise, such as those from the NOISEX-92 database =-=[144]-=-, can be added to the clean training data to generate noise-corrupted training data. This provides good results for levels of noise down to 6-10dB. However, matched training cannot easily address chan... |

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Citation Context ...ize of ∆ = 1 gives coefficients that are simply the difference between the previous and following frame. A large window size of ∆ = 2 gives a more robust estimate of dynamic coefficients. As noted in =-=[39]-=-, delta parameters may be considered an approximation to the first derivative of the static parameters; hence in the Continuous-Time approximation, time derivatives of the static coefficients may be u... |

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Citation Context ...k ∗ = argmax P(k|ot) (4.17) k ˆst = ot + ˇµ (k∗ ) (4.18) SPLICE is not intrinsically tied to stereo data; with a prior clean speech GMM, a corrupted speech GMM may be estimated using VTS compensation =-=[2, 106]-=- and the biases computed from the two GMM. Limiting the update of the feature vector to only a bias form is efficient, however a MLLR-like affine transform would be more accurate as suggested in Deng ... |

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Citation Context ...variances are not optimal for ASR. Recent ASR systems [32, 53, 121, 134] have demonstrated the importance of modelling correlations between dimensions by either using full adaptation transforms, HLDA =-=[87]-=-, or STC [41] techniques. Thus it is interesting to examine how environmental noise affects these intraframe correlations. Figure 3.5 shows contours of equal probability for full bivariate Gaussian di... |

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Citation Context ...sults, from an uncompensated system, were used during scoring for all the systems described. A more complex system would typically use some form of feature projection scheme such as HLDA [87] or fMPE =-=[116]-=-, advanced covariance modelling such as STC [41], and MMI [141] or MPE [115] training of model parameters—the use of such techniques were not investigated in these experiments.sCHAPTER 9. EXPERIMENTAL... |

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Citation Context ...s,t st − ˆµ (jm) �� s st − ˆµ (jm) s �T t=1 γ(jm) s,t � T (2.33) (2.34) (2.35) (2.36)sCHAPTER 2. HIDDEN MARKOV MODEL SPEECH RECOGNITION 15 Derivations for these solutions can be found in Huang et al. =-=[71, 72]-=-. To compute diagonal variances, as discussed on page 2.3.1, the full covariance is diagonalised ˆΣ (jm) s = diag � Σ ˆ (jm) � s,full (2.37) By using a mixture of Gaussians with diagonal covariances, ... |

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Citation Context ...racy are sensible evaluation criteria for transcription tasks, for other ASR systems it may not be an optimal guide to performance. For example, dialogue system evaluations may quote concept accuracy =-=[12]-=- or task completion rate. 2.5 Adaptation and Normalisation Despite the amount of data used to train the acoustic models and efforts to produce speaker independent systems, there is still degradation w... |

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Citation Context ...g C0, plus the first and second differentials. This yields a 39-dimensional feature vector. The WSJ SI284 training data was used to train a clean acoustic model in a similar manner to Woodland et al. =-=[148]-=-. There are 284 speakers from the WSJ0 and WSJ1 corpora yielding 66 hours of speech data. The acoustic models are decision tree clustered state, crossword triphones, with three-emitting states per HMM... |

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Citation Context ...hoice that assumes the sum of two log-normal distributions is approximately log-normal, however it cannot be applied with delta and delta-delta parameters due to the resulting complexity of the forms =-=[50]-=-. Another approximation is the log-add, which may be used to update the component means of the static dimensions µ l(m) y,i = log� exp(µ l(m) x,i ) + exp(µl z,i) � = µ l(m) x,i + log� 1 + exp(µ l z,i ... |

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Citation Context ...000 or more words. The number of words is rather arbitrary in the definitions, but they give a sense of task complexity. The optimal word sequence, or sometimes a word lattice [72], confusion network =-=[31]-=-, or list of possible transcriptions, is then passed to the application. The application may simply provide transcriptions, where post-processing could be required to add punctuation and capitalisatio... |

55 |
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Citation Context ...ly used speech parameterisation, its optimality has been questioned [63, 65, 67]. Alternatively, perceptual linear prediction (PLP) coefficients have been used [64] giving similar performance to MFCC =-=[69]-=-. An extensive review of speech signal representations can be found in Huang et al. [72] or Gold and Morgan [57].sCHAPTER 2. HIDDEN MARKOV MODEL SPEECH RECOGNITION 8 2.2.1 Dynamic Features The set of ... |

52 | Should recognizers have ears
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Citation Context ...sumptions that are tolerable with clean speech, such as the conditional independence of observations, and the lack of explicit duration modelling may result in increased fragility to noise. Hermansky =-=[66]-=- contends that the fragility of ASR in realistic situations is due to excessive attention to spectral structure and poor modelling of the temporal structure of speech signals. A frequent comparison is... |

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Citation Context ... discriminative training. Discriminative training focuses on estimating model parameters that minimise the error rate. An early form of discriminative training used a maximum mutual information (MMI) =-=[141]-=- criterion. MMI aims to optimise the posterior probability that a model generated a portion of the training utterance—this maximises the mutual information between the training data and the models. Wh... |

48 |
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Citation Context ...d noise speech statistics, by using the state rather than global statistics, for use in the enhancement process. Enhancement with auto-regressive, hidden Markov models of speech is studied in Ephraim =-=[28]-=-, Logan and Robinson [101], Seymour and Niranjan [128]. As discussed in [29], speech enhancement can be viewed as minimising the average distortion between an estimator of the clean speech vector ˇst ... |

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Citation Context ...combination (PMC) combines separate noise and speech models to form a corrupted speech model directly for use in the recognition process. It assumes the component posteriors remain unchanged in noise =-=[51]-=-. Therefore only the model component distributions need updating. In non-iterative forms of PMC, each clean speech model component is combined with the noise model via a mismatch function to yield an ... |

47 |
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Citation Context ..., channel distortions either due to the microphone or network with channel noise added, and finally possible noise at the near end of the speech recognition system. This is summarised in a model from =-=[62]-=- shown in figure 3.1. ���� � � y(τ)= x(τ) � Task Workload Stress Noise � zenv(τ) � � � + zenv(τ) ∗ hmic(τ) + zchan(τ) ∗ hchan(τ) + znear(τ) (3.1) Figure 3.1: Sources of noise and distortion that can e... |

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Multi-Style Training for Robust Isolated-Word Speech Recognition
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Citation Context ... may be applied to remove unwanted, non-linguistic factors, such as speaker differences or the acoustic environment, from being included in the acoustic models [3, 22, 43, 44]. In multistyle training =-=[98]-=- the acoustic model is forced to represent all these factors; a speaker independent model may be considered a multistyle model. Adaptive training instead uses transforms to model the variation from di... |