37 research outputs found
Low bit rate speech coding methods and a new interframe differential coding scheme for line spectrum pairs
Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1992.Thesis (Master's) -- Bilkent University, 1992.Includes bibliographical references leaves 30-32.Low bit rate speech coding techniques and a new coding scheme for vocal
tract parameters are presented. Linear prediction based voice coding techniques
(linear predictive coding and code excited linear predictive coding) are
examined and implemented. A new interframe differential coding scheme for
line spectrum pairs is developed. The new scheme reduces the spectral distortion
of the linear predictive filter while maintaining a high compression ratio.Erzin, EnginM.S
HuBERT-TR: Reviving Turkish Automatic Speech Recognition with Self-supervised Speech Representation Learning
While the Turkish language is listed among low-resource languages, literature
on Turkish automatic speech recognition (ASR) is relatively old. In this paper,
we present HuBERT-TR, a speech representation model for Turkish, based on
HuBERT. HuBERT-TR achieves state-of-the-art results on several Turkish ASR
datasets. We investigate pre-training HuBERT for Turkish with large-scale data
curated from online resources. We pre-train HuBERT-TR using over 6,500 hours of
speech data curated from YouTube that includes extensive variability in terms
of quality and genre. We show that language-specific models are superior to
other pre-trained models, where our Turkish model HuBERT-TR/base performs
better than the x10 times larger state-of-the-art multilingual XLS-R-1b model
in low-resource settings. Moreover, we study the effect of scaling on ASR
performance by scaling our models up to 1B parameters. Our best model yields a
state-of-the-art word error rate of 4.97% on the Turkish Broadcast News
dataset. Models are available at https://huggingface.co/asafayaComment: Submitted to ICASSP202
New methods for robust speech recognition
Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 1995.Thesis (Ph.D.) -- Bilkent University, 1995.Includes bibliographical references leaves 86-92.New methods of feature extraction, end-point detection and speech enhcincement
are developed for a robust speech recognition system.
The methods of feature extraction and end-point detection are based on
wavelet analysis or subband analysis of the speech signal. Two new sets of speech
feature parameters, SUBLSF’s and SUBCEP’s, are introduced. Both parameter
sets are based on subband analysis. The SUBLSF feature parameters are obtained
via linear predictive analysis on subbands. These speech feature parameters
can produce better results than the full-band parameters when the noise is
colored. The SUBCEP parameters are based on wavelet analysis or equivalently
the multirate subband analysis of the speech signal. The SUBCEP parameters
also provide robust recognition performance by appropriately deemphasizing the
frequency bands corrupted by noise. It is experimentally observed that the
subband analysis based feature parameters are more robust than the commonly
used full-band analysis based parameters in the presence of car noise.
The a-stable random processes can be used to model the impulsive nature of the public network telecommunication noise. Adaptive filtering are developed
for Q-stable random processes. Adaptive noise cancelation techniques are used to
reduce the mismacth between training and testing conditions of the recognition
system over telephone lines.
Another important problem in isolated speech recognition is to determine
the boundaries of the speech utterances or words. Precise boundary detection
of utterances improves the performance of speech recognition systems. A new
distance measure based on the subband energy levels is introduced for endpoint
detection.Erzin, EnginPh.D
Interframe differential vector coding of line spectrum frequencies
Line Spectrum Frequencies (LSF's) uniquely represent the Linear Predictive Coding (LPC) filter of a speech frame. In many vocoders LSF's are used to encode the LPC parameters. In this paper, an interframe differential coding scheme is presented for the LSF's. The LSF's of the current speech frame are predicted by using both the LSF's of the previous frame and some of the LSF's of the current frame. Then, the difference vector resulting from prediction is vector quantized
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Speech features for telemonitoring of Parkinson's disease symptoms.
The aim of this paper is tracking Parkinson's disease (PD) progression based on its symptoms on vocal system using Unified Parkinsons Disease Rating Scale (UPDRS). We utilize a standard speech signal feature set, which contains 6373 static features as functionals of low-level descriptor (LLD) contours, and select the most informative ones using the maximal relevance and minimal redundancy based on correlations (mRMRC) criteria. Then, we evaluate performance of Gaussian mixture regression (GMR) and support vector regression (SVR) on estimating the third subscale of UPDRS, i.e., UPDRS: motor subscale (UPDRS-III). Among the most informative features, a list of features are selected after redundancy reduction. The selected features depict that LLDs providing information about spectrum flatness, spectral distribution of energy, and hoarseness of voice are the most important ones for estimating UPDRS-III. Moreover, the most informative statistical functions are related to range, maximum, minimum and standard deviation of LLDs, which is an evidence of the muscle weakness due to the PD. Furthermore, GMR outperforms SVR on compact feature sets while the performance of SVR improves by increasing number of features