Abstract: Statement of the automatic speech recognition
problem, the assignment of speech recognition and the application
fields are shown in the paper. At the same time as Azerbaijan speech,
the establishment principles of speech recognition system and the
problems arising in the system are investigated. The computing algorithms of speech features, being the main part
of speech recognition system, are analyzed. From this point of view,
the determination algorithms of Mel Frequency Cepstral Coefficients
(MFCC) and Linear Predictive Coding (LPC) coefficients expressing
the basic speech features are developed. Combined use of cepstrals of
MFCC and LPC in speech recognition system is suggested to
improve the reliability of speech recognition system. To this end, the
recognition system is divided into MFCC and LPC-based recognition
subsystems. The training and recognition processes are realized in
both subsystems separately, and recognition system gets the decision
being the same results of each subsystems. This results in decrease of
error rate during recognition. The training and recognition processes are realized by artificial
neural networks in the automatic speech recognition system. The
neural networks are trained by the conjugate gradient method. In the
paper the problems observed by the number of speech features at
training the neural networks of MFCC and LPC-based speech
recognition subsystems are investigated. The variety of results of neural networks trained from different
initial points in training process is analyzed. Methodology of
combined use of neural networks trained from different initial points
in speech recognition system is suggested to improve the reliability
of recognition system and increase the recognition quality, and
obtained practical results are shown.
Abstract: Recently, Automatic Speech Recognition (ASR) systems were used to assist children in language acquisition as it has the ability to detect human speech signal. Despite the benefits offered by the ASR system, there is a lack of ASR systems for Malay-speaking children. One of the contributing factors for this is the lack of continuous speech database for the target users. Though cross-lingual adaptation is a common solution for developing ASR systems for under-resourced language, it is not viable for children as there are very limited speech databases as a source model. In this research, we propose a two-stage adaptation for the development of ASR system for Malay-speaking children using a very limited database. The two stage adaptation comprises the cross-lingual adaptation (first stage) and cross-age adaptation. For the first stage, a well-known speech database that is phonetically rich and balanced, is adapted to the medium-sized Malay adults using supervised MLLR. The second stage adaptation uses the speech acoustic model generated from the first adaptation, and the target database is a small-sized database of the target users. We have measured the performance of the proposed technique using word error rate, and then compare them with the conventional benchmark adaptation. The two stage adaptation proposed in this research has better recognition accuracy as compared to the benchmark adaptation in recognizing children’s speech.
Abstract: An automatic speech recognition system for the
formal Arabic language is needed. The Quran is the most formal
spoken book in Arabic, it is spoken all over the world. In this
research, an automatic speech recognizer for Quranic based speakerindependent
was developed and tested. The system was developed
based on the tri-phone Hidden Markov Model and Maximum
Likelihood Linear Regression (MLLR). The MLLR computes a set
of transformations which reduces the mismatch between an initial
model set and the adaptation data. It uses the regression class tree, as
well as, estimates a set of linear transformations for the mean and
variance parameters of a Gaussian mixture HMM system. The 30th
Chapter of the Quran, with five of the most famous readers of the
Quran, was used for the training and testing of the data. The chapter
includes about 2000 distinct words. The advantages of using the
Quranic verses as the database in this developed recognizer are the
uniqueness of the words and the high level of orderliness between
verses. The level of accuracy from the tested data ranged 68 to 85%.
Abstract: The Automatic Speech Recognition (ASR) applied to
Arabic language is a challenging task. This is mainly related to the
language specificities which make the researchers facing multiple
difficulties such as the insufficient linguistic resources and the very
limited number of available transcribed Arabic speech corpora. In
this paper, we are interested in the development of a HMM-based
ASR system for Standard Arabic (SA) language. Our fundamental
research goal is to select the most appropriate acoustic parameters
describing each audio frame, acoustic models and speech recognition
unit. To achieve this purpose, we analyze the effect of varying frame
windowing (size and period), acoustic parameter number resulting
from features extraction methods traditionally used in ASR, speech
recognition unit, Gaussian number per HMM state and number of
embedded re-estimations of the Baum-Welch Algorithm. To evaluate
the proposed ASR system, a multi-speaker SA connected-digits
corpus is collected, transcribed and used throughout all experiments.
A further evaluation is conducted on a speaker-independent continue
SA speech corpus. The phonemes recognition rate is 94.02% which is
relatively high when comparing it with another ASR system
evaluated on the same corpus.