Noise Robust Speaker-Independent Speech Recognition with Invariant-Integration Features Using Power-Bias Subtraction
نویسندگان
چکیده
This paper presents new results about the robustness of invariantintegration features (IIF) in noisy conditions. Furthermore, it is shown that a feature-enhancement method known as “powerbias subtraction” for noisy conditions can be combined with the IIF approach to improve its performance in noisy environments while keeping the robustness of the IIFs to mismatching vocaltract length training-testing conditions. Results of experiments with training on clean speech only as well as experiments with matched-condition training are presented.
منابع مشابه
Temporally Weighted Linear Prediction Features for Speaker Verification in Additive Noise
We consider text-independent speaker verification under additive noise corruption. In the popular mel-frequency cepstral coefficient (MFCC) front-end, we substitute the conventional Fourier-based spectrum estimation with weighted linear predictive methods, which have earlier shown success in noise-robust speech recognition. We introduce two temporally weighted variants of linear predictive (LP)...
متن کاملImproving the performance of MFCC for Persian robust speech recognition
The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...
متن کاملNoise-invariant representation for speech signals
A new group-delay based spectral domain is explored for representation of speech signals and for extraction of robust features. The spectrum is computed using the group-delay functions defined on the autocorrelation of a short segment of speech. The features derived from this spectrum are easy to compute and are robust to the background noise. The invariance of the spectral shape to noise in th...
متن کاملSpectral subtraction in noisy environments applied to speaker adaptation based on HMM sufficient statistics
Noise and speaker adaptation techniques are essential to realize robust speech recognition in real noisy environments . In this paper, we applied spectral subtraction to an unsupervised speaker adaptation algorithm in noisy environments. The adaptation algorithm consists of the following five steps. (1) Spectral subtraction is carried out for noise added database. (2) Noise matched acoustic mod...
متن کاملFeature Level Compensation for Robust Speaker Identification in Mismatched Conditions
In this paper, robust front end features are proposed for improvement in speaker identification (SI) performance by considering the factors of real world situations, like mismatch between training and testing conditions. The most commonly used MFCC features are very much sensitive to effects such as channel and environment mismatch. Characteristics of speech gets changed with room acoustics, ch...
متن کامل