246 research outputs found
Optimization of data-driven filterbank for automatic speaker verification
Most of the speech processing applications use triangular filters spaced in
mel-scale for feature extraction. In this paper, we propose a new data-driven
filter design method which optimizes filter parameters from a given speech
data. First, we introduce a frame-selection based approach for developing
speech-signal-based frequency warping scale. Then, we propose a new method for
computing the filter frequency responses by using principal component analysis
(PCA). The main advantage of the proposed method over the recently introduced
deep learning based methods is that it requires very limited amount of
unlabeled speech-data. We demonstrate that the proposed filterbank has more
speaker discriminative power than commonly used mel filterbank as well as
existing data-driven filterbank. We conduct automatic speaker verification
(ASV) experiments with different corpora using various classifier back-ends. We
show that the acoustic features created with proposed filterbank are better
than existing mel-frequency cepstral coefficients (MFCCs) and
speech-signal-based frequency cepstral coefficients (SFCCs) in most cases. In
the experiments with VoxCeleb1 and popular i-vector back-end, we observe 9.75%
relative improvement in equal error rate (EER) over MFCCs. Similarly, the
relative improvement is 4.43% with recently introduced x-vector system. We
obtain further improvement using fusion of the proposed method with standard
MFCC-based approach.Comment: Published in Digital Signal Processing journal (Elsevier
[[alternative]]Text-Independent Speaker Identification Systems Based on Multi-Layer Gaussian Mixture Models
計畫編號:NSC92-2213-E032-026研究期間:200308~200407研究經費:541,000[[sponsorship]]行政院國家科學委員
Bio-motivated features and deep learning for robust speech recognition
Mención Internacional en el título de doctorIn spite of the enormous leap forward that the Automatic Speech
Recognition (ASR) technologies has experienced over the last five years
their performance under hard environmental condition is still far from
that of humans preventing their adoption in several real applications.
In this thesis the challenge of robustness of modern automatic speech
recognition systems is addressed following two main research lines.
The first one focuses on modeling the human auditory system to
improve the robustness of the feature extraction stage yielding to novel
auditory motivated features. Two main contributions are produced.
On the one hand, a model of the masking behaviour of the Human
Auditory System (HAS) is introduced, based on the non-linear filtering
of a speech spectro-temporal representation applied simultaneously
to both frequency and time domains. This filtering is accomplished
by using image processing techniques, in particular mathematical
morphology operations with an specifically designed Structuring Element
(SE) that closely resembles the masking phenomena that take
place in the cochlea. On the other hand, the temporal patterns of
auditory-nerve firings are modeled. Most conventional acoustic features
are based on short-time energy per frequency band discarding
the information contained in the temporal patterns. Our contribution
is the design of several types of feature extraction schemes based on
the synchrony effect of auditory-nerve activity, showing that the modeling
of this effect can indeed improve speech recognition accuracy in
the presence of additive noise. Both models are further integrated into
the well known Power Normalized Cepstral Coefficients (PNCC).
The second research line addresses the problem of robustness in
noisy environments by means of the use of Deep Neural Networks
(DNNs)-based acoustic modeling and, in particular, of Convolutional
Neural Networks (CNNs) architectures. A deep residual network
scheme is proposed and adapted for our purposes, allowing Residual
Networks (ResNets), originally intended for image processing tasks,
to be used in speech recognition where the network input is small
in comparison with usual image dimensions. We have observed that
ResNets on their own already enhance the robustness of the whole system
against noisy conditions. Moreover, our experiments demonstrate
that their combination with the auditory motivated features devised
in this thesis provide significant improvements in recognition accuracy
in comparison to other state-of-the-art CNN-based ASR systems
under mismatched conditions, while maintaining the performance in
matched scenarios.
The proposed methods have been thoroughly tested and compared
with other state-of-the-art proposals for a variety of datasets and
conditions. The obtained results prove that our methods outperform
other state-of-the-art approaches and reveal that they are suitable for
practical applications, specially where the operating conditions are
unknown.El objetivo de esta tesis se centra en proponer soluciones al problema
del reconocimiento de habla robusto; por ello, se han llevado a cabo
dos líneas de investigación.
En la primera líınea se han propuesto esquemas de extracción de características novedosos, basados en el modelado del comportamiento
del sistema auditivo humano, modelando especialmente los fenómenos
de enmascaramiento y sincronía. En la segunda, se propone mejorar
las tasas de reconocimiento mediante el uso de técnicas de
aprendizaje profundo, en conjunto con las características propuestas.
Los métodos propuestos tienen como principal objetivo, mejorar la
precisión del sistema de reconocimiento cuando las condiciones de
operación no son conocidas, aunque el caso contrario también ha sido
abordado.
En concreto, nuestras principales propuestas son los siguientes:
Simular el sistema auditivo humano con el objetivo de mejorar
la tasa de reconocimiento en condiciones difíciles, principalmente
en situaciones de alto ruido, proponiendo esquemas de
extracción de características novedosos.
Siguiendo esta dirección, nuestras principales propuestas se detallan a continuación:
• Modelar el comportamiento de enmascaramiento del sistema
auditivo humano, usando técnicas del procesado de
imagen sobre el espectro, en concreto, llevando a cabo el
diseño de un filtro morfológico que captura este efecto.
• Modelar el efecto de la sincroní que tiene lugar en el nervio
auditivo.
• La integración de ambos modelos en los conocidos Power
Normalized Cepstral Coefficients (PNCC).
La aplicación de técnicas de aprendizaje profundo con el objetivo
de hacer el sistema más robusto frente al ruido, en particular
con el uso de redes neuronales convolucionales profundas, como
pueden ser las redes residuales.
Por último, la aplicación de las características propuestas en
combinación con las redes neuronales profundas, con el objetivo
principal de obtener mejoras significativas, cuando las condiciones
de entrenamiento y test no coinciden.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Javier Ferreiros López.- Secretario: Fernando Díaz de María.- Vocal: Rubén Solera Ureñ
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
A comparison of features for large population speaker identification
Bibliography: leaves 95-104.Speech recognition systems all have one criterion in common; they perform better in a controlled environment using clean speech. Though performance can be excellent, even exceeding human capabilities for clean speech, systems fail when presented with speech data from more realistic environments such as telephone channels. The differences using a recognizer in clean and noisy environments are extreme, and this causes one of the major obstacles in producing commercial recognition systems to be used in normal environments. It is the lack of performance of speaker recognition systems with telephone channels that this work addresses. The human auditory system is a speech recognizer with excellent performance, especially in noisy environments. Since humans perform well at ignoring noise more than any machine, auditory-based methods are the promising approaches since they attempt to model the working of the human auditory system. These methods have been shown to outperform more conventional signal processing schemes for speech recognition, speech coding, word-recognition and phone classification tasks. Since speaker identification has received lot of attention in speech processing because of its waiting real-world applications, it is attractive to evaluate the performance using auditory models as features. Firstly, this study rums at improving the results for speaker identification. The improvements were made through the use of parameterized feature-sets together with the application of cepstral mean removal for channel equalization. The study is further extended to compare an auditory-based model, the Ensemble Interval Histogram, with mel-scale features, which was shown to perform almost error-free in clean speech. The previous studies of Elli to be more robust to noise were conducted on speaker dependent, small population, isolated words and now are extended to speaker independent, larger population, continuous speech. This study investigates whether the Elli representation is more resistant to telephone noise than mel-cepstrum as was shown in the previous studies, when now for the first time, it is applied for speaker identification task using the state-of-the-art Gaussian mixture model system
Using a low-bit rate speech enhancement variable post-filter as a speech recognition system pre-filter to improve robustness to GSM speech
Includes bibliographical references.Performance of speech recognition systems degrades when they are used to recognize speech that has been transmitted through GS1 (Global System for Mobile Communications) voice communication channels (GSM speech). This degradation is mainly due to GSM speech coding and GSM channel noise on speech signals transmitted through the network. This poor recognition of GSM channel speech limits the use of speech recognition applications over GSM networks. If speech recognition technology is to be used unlimitedly over GSM networks recognition accuracy of GSM channel speech has to be improved. Different channel normalization techniques have been developed in an attempt to improve recognition accuracy of voice channel modified speech in general (not specifically for GSM channel speech). These techniques can be classified into three broad categories, namely, model modification, signal pre-processing and feature processing techniques. In this work, as a contribution toward improving the robustness of speech recognition systems to GSM speech, the use of a low-bit speech enhancement post-filter as a speech recognition system pre-filter is proposed. This filter is to be used in recognition systems in combination with channel normalization techniques
Investigation of the impact of high frequency transmitted speech on speaker recognition
Thesis (MScEng)--Stellenbosch University, 2002.Some digitised pages may appear illegible due to the condition of the original hard copy.ENGLISH ABSTRACT: Speaker recognition systems have evolved to a point where near perfect performance can be
obtained under ideal conditions, even if the system must distinguish between a large number
of speakers. Under adverse conditions, such as when high noise levels are present or when the
transmission channel deforms the speech, the performance is often less than satisfying.
This project investigated the performance of a popular speaker recognition system, that use
Gaussian mixture models, on speech transmitted over a high frequency channel. Initial experiments
demonstrated very unsatisfactory results for the base line system.
We investigated a number of robust techniques. We implemented and applied some of them in
an attempt to improve the performance of the speaker recognition systems. The techniques we
tested showed only slight improvements.
We also investigates the effects of a high frequency channel and single sideband modulation on
the speech features of speech processing systems. The effects that can deform the features, and
therefore reduce the performance of speech systems, were identified.
One of the effects that can greatly affect the performance of a speech processing system is
noise. We investigated some speech enhancement techniques and as a result we developed a
new statistical based speech enhancement technique that employs hidden Markov models to
represent the clean speech process.AFRIKAANSE OPSOMMING: Sprekerherkenning-stelsels het 'n punt bereik waar nabyaan perfekte resultate verwag kan word
onder ideale kondisies, selfs al moet die stelsel tussen 'n groot aantal sprekers onderskei. Wanneer
nie-ideale kondisies, soos byvoorbeeld hoë ruisvlakke of 'n transmissie kanaal wat die
spraak vervorm, teenwoordig is, is die resultate gewoonlik nie bevredigend nie.
Die projek ondersoek die werksverrigting van 'n gewilde sprekerherkenning-stelsel, wat gebruik
maak van Gaussiese mengselmodelle, op spraak wat oor 'n hoë frekwensie transmissie
kanaal gestuur is. Aanvanklike eksperimente wat gebruik maak van 'n basiese stelsel het nie
goeie resultate opgelewer nie.
Ons het 'n aantal robuuste tegnieke ondersoek en 'n paar van hulle geïmplementeer en getoets
in 'n poging om die resultate van die sprekerherkenning-stelsel te verbeter. Die tegnieke wat
ons getoets het, het net geringe verbetering getoon.
Die studie het ook die effekte wat die hoë-frekwensie kanaal en enkel-syband modulasie op
spraak kenmerkvektore, ondersoek. Die effekte wat die spraak kenmerkvektore kan vervorm en
dus die werkverrigting van spraak stelsels kan verlaag, is geïdentifiseer.
Een van die effekte wat 'n groot invloed op die werkverrigting van spraakstelsels het, is ruis.
Ons het spraak verbeterings metodes ondersoek en dit het gelei tot die ontwikkeling van 'n
statisties gebaseerde spraak verbeteringstegniek wat gebruik maak van verskuilde Markov modelle
om die skoon spraakproses voor te stel
Speech Recognition in noisy environment using Deep Learning Neural Network
Recent researches in the field of automatic speaker recognition have shown that methods based
on deep learning neural networks provide better performance than other statistical classifiers. On
the other hand, these methods usually require adjustment of a significant number of parameters.
The goal of this thesis is to show that selecting appropriate value of parameters can significantly
improve speaker recognition performance of methods based on deep learning neural networks.
The reported study introduces an approach to automatic speaker recognition based on deep
neural networks and the stochastic gradient descent algorithm. It particularly focuses on three
parameters of the stochastic gradient descent algorithm: the learning rate, and the hidden and
input layer dropout rates. Additional attention was devoted to the research question of speaker
recognition under noisy conditions.
Thus, two experiments were conducted in the scope of this thesis. The first experiment was
intended to demonstrate that the optimization of the observed parameters of the stochastic
gradient descent algorithm can improve speaker recognition performance under no presence of
noise. This experiment was conducted in two phases. In the first phase, the recognition rate is
observed when the hidden layer dropout rate and the learning rate are varied, while the input
layer dropout rate was constant. In the second phase of this experiment, the recognition rate is
observed when the input layers dropout rate and learning rate are varied, while the hidden layer
dropout rate was constant. The second experiment was intended to show that the optimization of
the observed parameters of the stochastic gradient descent algorithm can improve speaker
recognition performance even under noisy conditions. Thus, different noise levels were
artificially applied on the original speech signal
Exploration and Optimization of Noise Reduction Algorithms for Speech Recognition in Embedded Devices
Environmental noise present in real-life applications substantially degrades the performance of speech recognition systems. An example is an in-car scenario where a speech recognition system has to support the man-machine interface. Several sources of noise coming from the engine, wipers, wheels etc., interact with speech. Special challenge is given in an open window scenario, where noise of traffic, park noise, etc., has to be regarded. The main goal of this thesis is to improve the performance of a speech recognition system based on a state-of-the-art hidden Markov model (HMM) using noise reduction methods. The performance is measured with respect to word error rate and with the method of mutual information. The noise reduction methods are based on weighting rules. Least-squares weighting rules in the frequency domain have been developed to enable a continuous development based on the existing system and also to guarantee its low complexity and footprint for applications in embedded devices. The weighting rule parameters are optimized employing a multidimensional optimization task method of Monte Carlo followed by a compass search method. Root compression and cepstral smoothing methods have also been implemented to boost the recognition performance. The additional complexity and memory requirements of the proposed system are minimum. The performance of the proposed system was compared to the European Telecommunications Standards Institute (ETSI) standardized system. The proposed system outperforms the ETSI system by up to 8.6 % relative increase in word accuracy and achieves up to 35.1 % relative increase in word accuracy compared to the existing baseline system on the ETSI Aurora 3 German task. A relative increase of up to 18 % in word accuracy over the existing baseline system is also obtained from the proposed weighting rules on large vocabulary databases. An entropy-based feature vector analysis method has also been developed to assess the quality of feature vectors. The entropy estimation is based on the histogram approach. The method has the advantage to objectively asses the feature vector quality regardless of the acoustic modeling assumption used in the speech recognition system
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