22 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
Improving the robustness of the usual fbe-based asr front-end
All speech recognition systems require some form of signal representation that parametrically models the
temporal evolution of the spectral envelope. Current parameterizations involve, either explicitly or implicitly, a
set of energies from frequency bands which are often distributed in a mel scale. The computation of those filterbank
energies (FBE) always includes smoothing of basic spectral measurements and non-linear amplitude
compression. A variety of linear transformations are typically applied to this time-frequency representation prior
to the Hidden Markov Model (HMM) pattern-matching stage of recognition. In the paper, we will discuss some
robustness issues involved in both the computation of the FBEs and the posterior linear transformations,
presenting alternative techniques that can improve robustness in additive noise conditions. In particular, the root
non-linearity, a voicing-dependent FBE computation technique and a time&frequency filtering (tiffing)
technique will be considered. Recognition results for the Aurora database will be shown to illustrate the potential
application of these alternatives techniques for enhancing the robustness of speech recognition systems.Peer ReviewedPostprint (published version
Wavelet-based techniques for speech recognition
In this thesis, new wavelet-based techniques have been developed for the
extraction of features from speech signals for the purpose of automatic speech
recognition (ASR). One of the advantages of the wavelet transform over the short
time Fourier transform (STFT) is its capability to process non-stationary signals.
Since speech signals are not strictly stationary the wavelet transform is a better
choice for time-frequency transformation of these signals. In addition it has
compactly supported basis functions, thereby reducing the amount of
computation as opposed to STFT where an overlapping window is needed. [Continues.
Reconocimiento del locutor mediante filtrado frecuencial de energías espectrales estimadas por métodos híbridos
Se han explorado dos formas de obtener parámetros más robustos para reconocimiento del locutor: la hibridación de técnicas de análisis espectral y el filtrado frecuencial de las energías de las bandas. Se ha comprobado que el filtrado frecuencial constituye una representación eficiente en reconocimiento del habla y puede ecualizar aproximadamente la varianza cepstral, realzando las oscilaciones espectrales más efectivas para la discriminación entre locutores. Se
han obtenido buenos resultados de identificación sobre la base de datos TIMIT, especialmente cuando se ha añadido ruido blanco. Por otro lado, se ha explorado la hibridación de la predicción lineal y el banco de filtros en la etapa de análisis espectral. La combinación de estas técnicas ha proporcionado buenos resultados de verificación sobre la base de datos telefónica POLYCOST.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
Speech recognition on DSP: algorithm optimization and performance analysis.
Yuan Meng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 85-91).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- History of ASR development --- p.2Chapter 1.2 --- Fundamentals of automatic speech recognition --- p.3Chapter 1.2.1 --- Classification of ASR systems --- p.3Chapter 1.2.2 --- Automatic speech recognition process --- p.4Chapter 1.3 --- Performance measurements of ASR --- p.7Chapter 1.3.1 --- Recognition accuracy --- p.7Chapter 1.3.2 --- Complexity --- p.7Chapter 1.3.3 --- Robustness --- p.8Chapter 1.4 --- Motivation and goal of this work --- p.8Chapter 1.5 --- Thesis outline --- p.10Chapter 2 --- Signal processing techniques for front-end --- p.12Chapter 2.1 --- Basic feature extraction principles --- p.13Chapter 2.1.1 --- Pre-emphasis --- p.13Chapter 2.1.2 --- Frame blocking and windowing --- p.13Chapter 2.1.3 --- Discrete Fourier Transform (DFT) computation --- p.15Chapter 2.1.4 --- Spectral magnitudes --- p.15Chapter 2.1.5 --- Mel-frequency filterbank --- p.16Chapter 2.1.6 --- Logarithm of filter energies --- p.18Chapter 2.1.7 --- Discrete Cosine Transformation (DCT) --- p.18Chapter 2.1.8 --- Cepstral Weighting --- p.19Chapter 2.1.9 --- Dynamic featuring --- p.19Chapter 2.2 --- Practical issues --- p.20Chapter 2.2.1 --- Review of practical problems and solutions in ASR appli- cations --- p.20Chapter 2.2.2 --- Model of environment --- p.23Chapter 2.2.3 --- End-point detection (EPD) --- p.23Chapter 2.2.4 --- Spectral subtraction (SS) --- p.25Chapter 3 --- HMM-based Acoustic Modeling --- p.26Chapter 3.1 --- HMMs for ASR --- p.26Chapter 3.2 --- Output probabilities --- p.27Chapter 3.3 --- Viterbi search engine --- p.29Chapter 3.4 --- Isolated word recognition (IWR) & Connected word recognition (CWR) --- p.30Chapter 3.4.1 --- Isolated word recognition --- p.30Chapter 3.4.2 --- Connected word recognition (CWR) --- p.31Chapter 4 --- DSP for embedded applications --- p.32Chapter 4.1 --- "Classification of embedded systems (DSP, ASIC, FPGA, etc.)" --- p.32Chapter 4.2 --- Description of hardware platform --- p.34Chapter 4.3 --- I/O operation for real-time processing --- p.36Chapter 4.4 --- Fixed point algorithm on DSP --- p.40Chapter 5 --- ASR algorithm optimization --- p.42Chapter 5.1 --- Methodology --- p.42Chapter 5.2 --- Floating-point to fixed-point conversion --- p.43Chapter 5.3 --- Computational complexity consideration --- p.45Chapter 5.3.1 --- Feature extraction techniques --- p.45Chapter 5.3.2 --- Viterbi search module --- p.50Chapter 5.4 --- Memory requirements consideration --- p.51Chapter 6 --- Experimental results and performance analysis --- p.53Chapter 6.1 --- Cantonese isolated word recognition (IWR) --- p.54Chapter 6.1.1 --- Execution time --- p.54Chapter 6.1.2 --- Memory requirements --- p.57Chapter 6.1.3 --- Recognition performance --- p.57Chapter 6.2 --- Connected word recognition (CWR) --- p.61Chapter 6.2.1 --- Execution time consideration --- p.62Chapter 6.2.2 --- Recognition performance --- p.62Chapter 6.3 --- Summary & discussion --- p.66Chapter 7 --- Implementation of practical techniques --- p.67Chapter 7.1 --- End-point detection (EPD) --- p.67Chapter 7.2 --- Spectral subtraction (SS) --- p.71Chapter 7.3 --- Experimental results --- p.72Chapter 7.3.1 --- Isolated word recognition (IWR) --- p.72Chapter 7.3.2 --- Connected word recognition (CWR) --- p.75Chapter 7.4 --- Results --- p.77Chapter 8 --- Conclusions and future work --- p.78Chapter 8.1 --- Summary and Conclusions --- p.78Chapter 8.2 --- Suggestions for future research --- p.80Appendices --- p.82Chapter A --- "Interpolation of data entries without floating point, divides or conditional branches" --- p.82Chapter B --- Vocabulary for Cantonese isolated word recognition task --- p.84Bibliography --- p.8