8,053 research outputs found
Speaker recognition using frequency filtered spectral energies
The spectral parameters that result from filtering the
frequency sequence of log mel-scaled filter-bank energies
with a simple first or second order FIR filter have proved
to be an efficient speech representation in terms of both
speech recognition rate and computational load. Recently,
the authors have shown that this frequency filtering can
approximately equalize the cepstrum variance enhancing
the oscillations of the spectral envelope curve that are
most effective for discrimination between speakers. Even
better speaker identification results than using melcepstrum
have been obtained on the TIMIT database,
especially when white noise was added. On the other
hand, the hybridization of both linear prediction and
filter-bank spectral analysis using either cepstral
transformation or the alternative frequency filtering has
been explored for speaker verification. The combination
of hybrid spectral analysis and frequency filtering, that
had shown to be able to outperform the conventional
techniques in clean and noisy word recognition, has yield
good text-dependent speaker verification results on the
new speaker-oriented telephone-line POLYCOST
database.Peer ReviewedPostprint (published version
Speech Recognition
Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes
Maximum likelihood Linear Programming Data Fusion for Speaker Recognition
Biometric system performance can be improved by means of data fusion. Several kinds of
information can be fused in order to obtain a more accurate classification (identification or
verification) of an input sample. In this paper we present a method for computing the
weights in a weighted sum fusion for score combinations, by means of a likelihood model.
The maximum likelihood estimation is set as a linear programming problem. The scores are
derived from a GMM classifier working on a different feature extractor. Our experimental
results assesed the robustness of the system in front a changes on time (different sessions)
and robustness in front a change of microphone. The improvements obtained were
significantly better (error bars of two standard deviations) than a uniform weighted sum or a
uniform weighted product or the best single classifier. The proposed method scales
computationaly with the number of scores to be fussioned as the simplex method for linear
programming
Kalman tracking of linear predictor and harmonic noise models for noisy speech enhancement
This paper presents a speech enhancement method based on the tracking and denoising of the formants of a linear prediction (LP) model of the spectral envelope of speech and the parameters of a harmonic noise model (HNM) of its excitation. The main advantages of tracking and denoising the prominent energy contours of speech are the efficient use of the spectral and temporal structures of successive speech frames and a mitigation of processing artefact known as the âmusical noiseâ or âmusical tonesâ.The formant-tracking linear prediction (FTLP) model estimation consists of three stages: (a) speech pre-cleaning based on a spectral amplitude estimation, (b) formant-tracking across successive speech frames using the Viterbi method, and (c) Kalman filtering of the formant trajectories across successive speech frames.The HNM parameters for the excitation signal comprise; voiced/unvoiced decision, the fundamental frequency, the harmonicsâ amplitudes and the variance of the noise component of excitation. A frequency-domain pitch extraction method is proposed that searches for the peak signal to noise ratios (SNRs) at the harmonics. For each speech frame several pitch candidates are calculated. An estimate of the pitch trajectory across successive frames is obtained using a Viterbi decoder. The trajectories of the noisy excitation harmonics across successive speech frames are modeled and denoised using Kalman filters.The proposed method is used to deconstruct noisy speech, de-noise its model parameters and then reconstitute speech from its cleaned parts. Experimental evaluations show the performance gains of the formant tracking, pitch extraction and noise reduction stages
Studies on noise robust automatic speech recognition
Noise in everyday acoustic environments such as cars, traffic environments, and cafeterias remains one of the main challenges in automatic speech recognition (ASR). As a research theme, it has received wide attention in conferences and scientific journals focused on speech technology. This article collection reviews both the classic and novel approaches suggested for noise robust ASR. The articles are literature reviews written for the spring 2009 seminar course on noise robust automatic speech recognition (course code T-61.6060) held at TKK
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