AbstractThe hidden Markov (HMM) and speech recognition algorithm based this model were studied in the paper. In addition the model and recognition algorithm of HMM got be improved based on the traditional the HMM. In the process of modeling, through the training of multiple observe sequence to achieve the recognition of non-specific people, and according to the different number of HMM states to establish the double-template of rough and high precision, and through the second matching algorithm to achieve higher recognition rate. A speech recognition system combined MFCC parameters and HMM algorithm was constructed based improved HMM algorithm. Experimental result shown the speech recognition rate of large vocabulary of non-specific people was greatly improved

Zhang, Jing

Zhang, Min

Elsevier - Publisher Connector 

A Speech Recognition System Based Improved Algorithm of Dual-template HMM 

 Procedia Engineering  15 ( 2011 )  2286 – 2290 
Available online at www.sciencedirect.com
1877-7058 © 2011 Published by Elsevier Ltd.
doi: 10.1016/j.proeng.2011.08.428 
A Speech Recognition System Based Improved Algorithm of 
Dual-template HMM 
JingZhanga,b  Min Zhangc a* 
a Faculty of Automation, Guangdong University of technology, Guangzhou Guangdong 510006, China 
bDept. of Computer Science and Technology, Guangdong University of Foreign Studies, Guangzhou  Guangdong  510006, China
c Faculty of information, Guangdong University of technology, Guangzhou Guangdong 510006, China 
Abstract 
The hidden Markov (HMM) and speech recognition algorithm based this model were studied in the paper. In addition 
the model and recognition algorithm of HMM got be improved based on the traditional the HMM. In the process of 
modeling, through the training of multiple observe sequence to achieve the recognition of non-specific people, and 
according to the different number of HMM states to establish the double-template of rough and high precision, and 
through the second matching algorithm to achieve higher recognition rate. A speech recognition system combined 
MFCC parameters and HMM algorithm was constructed based improved HMM algorithm. Experimental result 
shown the speech recognition rate of large vocabulary of non-specific people was greatly improved. 
© 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011] 
Keywords:Speech Recognition; Hidden Markov Model; Double Template Matching; Multiple observe sequence; Data overflow 
1. Introduction 
Speech signal is smooth and time-invariant in a very short analysis interval, and the statistical feature can be 
described by the parameters of classical linear model, but for an overall speech signal, the signal is time-varying and 
the parameters of linear model also would change. As a statistical model, HMM could both represent the information 
of short-term smooth linear model, and describe the transition between each model. Therefore, HMM was used to 
build statistical models for the speech signal: one is Markov chain with a finite number of states used to simulate the 
implied stochastic process of the speech signal with changing statistical properties, and the other is the stochastic 
process of observation sequence associated with each state of Markov chain, as an ideal solution of speech 
recognition, the HMM completely expressed the acoustic model of speech. However, for the practical application of 
HMM in speech recognition process there are still many problems, such as the large number of observation sequences 
involved in the training,  the estimate of initial parameters, and so on, the key of improving speech recognition rate is 
how to resolve these issues.  
 
* Corresponding author. Tel.: +-0-020-61990270 
E-mail address: ha_go@163.com. 
Open access under CC BY-NC-ND license.
Open access under CC BY-NC-ND license.
2287 Jing Zhang and Min Zhang /  Procedia Engineering  15 ( 2011 )  2286 – 2290 
L
2. The improved HMM model and recognition algorithm 
2.1. The improved HMM models ---- multiple observations sequence modeling 
For the classic Baum-Welch algorithm, the parameter revaluation formula was deduced under the 
condition of assuming only one observation sequence. In the application, there are a large number of 
observation sequence involved in training, that is, for each HMM model, a large number of speech data 
will be collected, the respective sequence of MFCC parameters should be calculated, and then used for 
the parameters revaluation of corresponding HMM.  
For example, for the building HMM models of word "ball", it should find a lot of people, and record 
multiple wav files for "ball" of everybody, after the endpoint detection, then to calculate parameters 
sequence of MFCC, that is so-called the observation sequence, then the Parameters of the model can be 
trained. 
In actual application, usually more than one observation sequence were used to train a HMM, then 
when train a HMM with L observations sequence, the revaluation formula of Baum-Welch algorithm 
should to be amended. Assuming L observations sequence were , in 
which , and assuming each observation sequence was independent, then got 
formula (1): 
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=l 1Since the revaluation formula was based on the frequency of different time, therefore, foe L a training 
sequences, a revised revaluation shown as the formula (2),(3),(4). 
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2.2. The improved recognition algorithm of HMM-----double-template matching 
The most important issue of HMM model training is the estimates of initial parameters. Different initial 
training may produce different results and the appropriateness of initial estimates is also related to the 
final model parameters can whether converge to the global optimum or not. The improvement for 
traditional HMM recognition algorithm is mainly adopting two-template matching method for different 
templates initialization.
For a speech model, the parameters of continuous HMM are more than that of discrete HMM, and it 
could characterize the spatial distribution of feature vector more accurately. But its problems are large 
calculation and the convergence is slow. 
2288   Jing Zhang and Min Zhang /  Procedia Engineering  15 ( 2011 )  2286 – 2290 
One of the prominent performance is, in theory, the model can be achieved convergence after finite 
iterations of any initial value. In fact, if improper initial value were chosen, combined with the imprecise 
characterization of model parameters, which may cause iteration divergence or excessive iterations and 
result in long training time or training results do not converge, then the requirement of actual application 
couldn’t be achieved. 
It was found in the research and experiment that the initial value selection of model parameters ʌ and A 
had little effect, in general, it was selected randomly or meanly. In the traditional HMM model the 
initialization of the B is generally taken to mean. That to divide observation vector sequences of each 
primitive to be identified into N segments (N is the number of states.). Since HMM model has the 
structure from left to right, the observation vector is corresponding to each state in time. After Segments 
dividing, each observation vector corresponds to a state of HMM, and then to calculate the mean 
correlation, variance and other parameters of each section. But the initial processing of B according to this 
approach has a certain relationship with the selection of the states quantity, that the fewer states, the rough 
division, and the characteristics of the speech signal could not be well reflected. 
The speech recognition is mainly pattern matching through each HMM template in system, that the 
pattern matching by Viterbi algorithm, then select the speech that was closest to the speech to be 
recognized as the recognition results according to matching probability. Therefore, in the implementation 
process of system the multi-template matching algorithm was proposed, that is, through two matches to 
achieve speech recognition and the improvement of recognition accuracy.  
The basic idea of the twice matching algorithms is: the system trained twice, and the training was 
divided into the HMM template of rough accuracy and high precision, the number of states N in the 
HMM of high precision is larger than that of rough accuracy. 
In the recognition process, firstly to match the speech to be recognized with the HMM parameters of 
rough accuracy, and then according to the recognition result, take the N recognition results with high 
similarity probability as the basis of the template need to be matched when selecting the high accuracy 
template, and then to the second match with the template with high accuracy, thereby to improve speech 
recognition accuracy.  
In theory, the larger number state is the better because that the error rate of recognition will be reduced 
to a stable level with the increase in the number of states. However, since the training samples is limited, 
therefore, the number of states N can not be too large, otherwise, the training many of the corresponding 
items of states in the parameter Ȝ = (A, B, ʌ) would be 0 or very close to 0 as redundancy, the 
experimental number of speech states fixed ranging from 3 to 8 according to the complexity. Taken 
English number “0-9” voice as test data, experiments were carried with different number of states, Table 
1 shown the average experimental results of three times recognition with the templates of different states 
number. 
It can be drawn from the data in Table 1 that with the increasing number of states N, the system 
recognition rate increased accordingly, but as the same time, the recognition time system required also 
increased, which is due to the calculation of the system increased, that the complexity of the system 
increased. 
Table 1 Recognition results of the templates with different states number 
2289 Jing Zhang and Min Zhang /  Procedia Engineering  15 ( 2011 )  2286 – 2290 
Number of states Number of templates 
Number of average 
recognition 
Average recognition 
rate 
Average recognition 
time 
3 10 9.003 90.03% 0.2806 
4 10 9.012 90.12% 0.2913 
5 10 9.223 92.23% 0.3122 
6 10 9.311 93.11% 0.3143 
7 10 9.345 93.45% 0.3221 
8 10 9.371 93.71% 0.3219 
3. System evaluation 
The recognition module of system was carried by using dual-template matching. In the recognition 
process, in order to improve the recognition efficiency, the two templates was not used for each 
recognition, if the first identification has been successful, template of rough accuracy used only, only the 
first unsuccessful recognition or the recognition result was not among the top two results then the second 
recognition ("re- recognition "or "twice recognition ")used. As Fig.1 shown, it is successful after twice 
recognition. 

Fig.1. result of twice recognition  
The multi-template matching of four categories of sports vocabulary in the training window were 
carried, and the experimental data as shown in Table 2 
Table 2 the recognition result of dual-template matching  
category 
The average 
recognition rate of 
once recognition 
The average recognition 
time of once recognition 
The average 
recognition rate of 
re-recognition 
The average 
recognition time of re-
recognition 
Basketball class 90.12% 0.2903 92.14% 0.3249 
Swimming class 89.76% 0.2899 91.35% 0.3197 
Track and Field class 88.57% 0.2917 90.68% 0.3092 
From the data in Table 3 can be seen, after using dual-template, the recognition rate of system has been 
increased, but it is same to the recognition time, the overall average recognition rate of the system reached 
90% or more, which meet the requirements of System performance. 
2290   Jing Zhang and Min Zhang /  Procedia Engineering  15 ( 2011 )  2286 – 2290 
Conclusion  
A dual-template matching method was proposed after the improvement of HMM algorithm aimed at 
the large vocabulary of non-specific, and some effective solutions about such issues as the initial model 
selection of HMM algorithm encountered in the practical application of, multiple observations sequence 
involved in the training and Data underflow, which made the speech recognition rate of large vocabulary 
of non-specific achieved more than 90% in the PC platform. However, although the system using HMM 
algorithm has some advantages, but there are also some disadvantages, such as with the number of 
recognition template increased, the total states umber of HMM model also increased exponentially, which 
will require large storage space. In order to get robust and can well applied to the model of embedded 
systems; some state clustering method need be studied. 
Acknowledgements 
 This work is partially supported by The ministry of education of humanities and social science project 
#10YJCZH220. 
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A Speech Recognition System Based Improved Algorithm of Dual-template HMM

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