# The Application of Speech Synthesis Technology Based on Deep Neural Network in Intelligent Broadcasting

Authors: Jihong Yang
Journal: Journal of Control Science and Engineering
 (2022)

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## Abstract

To improve the sound quality of speech synthesis technology in intelligent broadcasting, a deep neural network-based method is proposed. It also proved the effectiveness of the DNN discrimination s/u/v and completed the conversion of the HMM synthesis spectrum parameter to original speech. Further, the scheme for transforming the parameters obtained from the temporary decomposition (TD) algorithm, DNN trains the event vectors obtained from TD decomposition, establishes the transformation model, and recombines with the untransformed event function. Experiments proved that the conversion effect of 16 dimensional parameters is not very ideal in subjective evaluation due to the fact that too few dimensions lead to insufficient spectral details, and the distortion in the process of further synthesis; the parameter conversion of 48 dimensions is slightly better than 16 dimensions, mainly due to more spectral details, but on the other hand, the influence of codebook mapping also affects the sound instability to some extent. It proves that the intelligent voice broadcast system completely solves these problems, which not only reduces construction costs, but also improves service efficiency.


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## Body

## 1. Introduction

Voice synthesis technology has been widely used in voice broadcasting in the fields of telecommunications, transportation, and banking, e.g., the queuing system of the telecom business hall, CRM (customer relationship management) system, billing system, the queuing system of the waiting hall broadcasting system of the transportation industry, and the queuing system of the business hall of the customers of the bank. With the rapid development of the voice and signal processing technology, the voice broadcasting application has been fully demonstrated by in various industries [1] with the increasing competition for the quality of services in various industries. The industry is also in increasing demand for intelligent voice broadcasting. For example, in the communication industry, after entering the accelerated construction stage of 4G network, people’s requirements for service quality are also getting higher and higher. Traditional artificial voice broadcasting can no longer meet the needs of users. At present, telecom operators also take improving the overall service level as one of the important means to attract customers, and it has invested a lot of energy and money. For example, in the phone fee inquiry, ticket booking, voice information broadcast, and other services, the investment is more, but the effect is not clear. How to build an efficient, characteristic, and professional intelligent voice broadcasting system is still a great challenge. Generally speaking, traditional artificial voice broadcasting has the following problems in [2]. Pronunciation reading is not standard, such as local mandarin mixed, iambic man for vertical three man irregular. Pronunciations are easy to be fallible, for example, in the business hall and waiting room, airport, and other places, where daily information need to be broadcast. A large number of users need to know the information of train arrival and departure as well as the temporary information of looking for things, so the phenomenon of misreading, misreading, and misbroadcasting is inevitable. A lot of repetitive work every day makes the announcer’s mental state poor and listless, and wastes manpower. You need to arrange many full-time rotating broadcasts for management costs.Speech synthesis technology is a technology involving multiple disciplines, including acoustics, linguistics, computer science, and digital signal processing. It is mainly committed to making robots speak like people, and specifically transforming originally visual text information into audible sound information [3]. Thus, research continues. Zhao, X. combined the deep neural network bidirectional RNN model for the prediction of Chinese prosodic words, and the prediction results show that the bidirectional RNN model with the attention mechanism can obtain a relatively accurate effect in predicting prosodic words [4]. Wang, D.’s research focused on the prediction of the prosodic word based on the prosodic structure prediction. Only by accurately predicting rhythmic words and then predicting other rhythmic structures can highly natural speech be synthesized in speech synthesis [5]. Reddy, M.K. used circulating neural networks (RNN) in deep neural networks and stated that they can handle the sequence prediction problem of seq2seq, and RNN is introduced here to predict for prosodic words [6]. In order to improve the sound quality of the HMM-based speech synthesis, with a small amount of data to train different parameters, obtain the resynthesis to achieve the effect of improving synthetic sound quality.


## 2. Speech Synthesis System for the Markov Model

A system where hidden Markov models model speech parameters for speech synthesis is one of the most widely used methods for speech synthesis research based on statistical parameter modeling. The hidden Markov model is a double embedded stochastic process, a random process describing state transfer which is similar to changes in speech, short stationary and implicitly unobservable; acoustic parameter synthesis needs to be estimated by observable sequence; another stochastic process describes the correspondence between the state and the observation, which just simulates a correspondence between the observed speech signal sequence and the synthetic parameters hidden under this. Therefore, the HMM is in line with the human speech generation mechanism and is a suitable model for speech signal analysis processing. The role of the training part of the HTS is that the original corpus is processed and trained with the model. The choice of the modeling mode is the number of states; because of the temporal properties of the speech, the number of states of a model will affect the duration of each state, generally determined according to the motif. The motif of phonemes or semi-syllables use the 5-state HMM; however, syllables generally use the 10-state HMM [7]. In practical modeling, for model simplification, the transition matrix in the HMM can be replaced by a duration model (dur) to constitute a semi-hidden Markov model. Joint modeling of clear turbidity segments by a multispatial probability distribution yields good results. The synthetic part of the HTS is equivalent to the inverse process of the training part, acting in generating the parameters by the already trained HMM under the guidance of the input text, and ultimately the speech waveform. The specific process is:(1)
The context information required for the synthesis is obtained through certain grammar rules and linguistic rules, which are annotated in the synthetic label. The decision tree decision to be synthesized is made after the training part, and the most similar leaf node HMM is the decision of the model.(2)
The decision-based model solves the synthetic base frequency and spectral parameters. The number of frames of each state is obtained from the length of the time, and the values of the parameters within the duration of the base frequency and the variance of the spectral model, combined with the dynamic characteristics, are the synthetic parameters.(3)
The source-one filter model is constructed from the solved parameters to synthesize the speech. The source was selected as described above: for the base frequency band, a single frequency pulse sequence corresponding to the base frequency is used as excitation; for the no base frequency band, Gaussian white noise is used as excitation.


## 3. Speech Synthesis Based on Deep Neural Networks

According to the current status of deep neural networks and the disadvantages of the HTS itself, this paper proposes a method to transform the parameters of the deep neural network to improve the synthesis effect of the HTS. Just with the appropriate transformation, an effectively trained model is capable of synthesizing a variety of timbre sounds.


### 3.1. Parameter Conversion of the Speech Synthesis Strategy

Synthetic speech and the original corpus are regarded as two independent speakers, with parameters as sources and target vectors, i.e., representing a global parameter mapping relationship from the synthetic speech to the original corpus by a deep neural network.LSF has characteristics by order(1)0<ω1<θ1<ω2<…<ωp/2<θp/2<π.The difference between the adjacent parameters is always greater than zero, where the parameters are dense, indicating the presence of a resonance peak in this frequency band and a trough with sparse parameters, which also visually represents the spectrum distribution while ensuring the stability of the LPC synthesis filter. Experiments were considered using the LSF parameters as the training parameters. According to the model motif of the HTS and the scale of the existing training corpus, we use the syllable to map the network model for the unit, which is to establish a deep neural network for each syllable (single word) of the source target to transform [8].Parameters of the same dimension were normalized to expand the variability of the parameters between frames and frames. Unlike previously, transformed parameters also need to be counternormalized and reduced for synthetic transformed speech after deep neural networks. In this paper, only the 20 sets of data closest to the target parameter were selected for transformation and a transformation/replacement scheme was replaced directly with the nearest vector in the target corpus when the conversion parameters do not fit order by order. In addition, increasing the learning efficiency adjustment with an adaptive step length accelerates the overall tuning of the network to prevent the difficulty of convergence to a minimum due to the inappropriate learning efficiency. The specific scheme is to compare the error distance between two for each tuning based on the initial learning efficiency, and reduce the learning efficiency to 0.9 times if the adjusted error distance exceeds a previous value (here set to 1.03 based on experience).


### 3.2. Parameter Conversion of the Speech Synthesis Architecture

The architecture of the system can be divided into two parts: network training phase and transformation synthesis phase. First, from the annotation information of the parameters to be converted, the spectrum parameters of the parallel corpus of the same model unit (syllable) are selected from the synthetic speech and the original corpus, and normalized by dimension after time alignment. The resulting normalization parameters are learned as the input and output parameters of the deep neural network. Get the source one-target conversion network for each syllable [9].The corresponding deep neural network is first selected by the annotation information to be converted based on the parameters for conversion, while the spectrum of the synthetic and original corpus is selected for unified normalization processing. The normalized spectrum to be converted serves as the input parameters of the corresponding converted network, and the converted spectrum is output through the deep neural network. To judge whether the conversion spectrum has the characteristics of order, replace the frame with the spectrum of the original corpus, obtain a stable conversion spectrum, and finally synthesize through the filter. In this way, the steps of training, transformation, and synthesis can also be completed under the condition of limited training parameters to achieve the effect of improving sound quality.


## 3.1. Parameter Conversion of the Speech Synthesis Strategy

Synthetic speech and the original corpus are regarded as two independent speakers, with parameters as sources and target vectors, i.e., representing a global parameter mapping relationship from the synthetic speech to the original corpus by a deep neural network.LSF has characteristics by order(1)0<ω1<θ1<ω2<…<ωp/2<θp/2<π.The difference between the adjacent parameters is always greater than zero, where the parameters are dense, indicating the presence of a resonance peak in this frequency band and a trough with sparse parameters, which also visually represents the spectrum distribution while ensuring the stability of the LPC synthesis filter. Experiments were considered using the LSF parameters as the training parameters. According to the model motif of the HTS and the scale of the existing training corpus, we use the syllable to map the network model for the unit, which is to establish a deep neural network for each syllable (single word) of the source target to transform [8].Parameters of the same dimension were normalized to expand the variability of the parameters between frames and frames. Unlike previously, transformed parameters also need to be counternormalized and reduced for synthetic transformed speech after deep neural networks. In this paper, only the 20 sets of data closest to the target parameter were selected for transformation and a transformation/replacement scheme was replaced directly with the nearest vector in the target corpus when the conversion parameters do not fit order by order. In addition, increasing the learning efficiency adjustment with an adaptive step length accelerates the overall tuning of the network to prevent the difficulty of convergence to a minimum due to the inappropriate learning efficiency. The specific scheme is to compare the error distance between two for each tuning based on the initial learning efficiency, and reduce the learning efficiency to 0.9 times if the adjusted error distance exceeds a previous value (here set to 1.03 based on experience).


## 3.2. Parameter Conversion of the Speech Synthesis Architecture

The architecture of the system can be divided into two parts: network training phase and transformation synthesis phase. First, from the annotation information of the parameters to be converted, the spectrum parameters of the parallel corpus of the same model unit (syllable) are selected from the synthetic speech and the original corpus, and normalized by dimension after time alignment. The resulting normalization parameters are learned as the input and output parameters of the deep neural network. Get the source one-target conversion network for each syllable [9].The corresponding deep neural network is first selected by the annotation information to be converted based on the parameters for conversion, while the spectrum of the synthetic and original corpus is selected for unified normalization processing. The normalized spectrum to be converted serves as the input parameters of the corresponding converted network, and the converted spectrum is output through the deep neural network. To judge whether the conversion spectrum has the characteristics of order, replace the frame with the spectrum of the original corpus, obtain a stable conversion spectrum, and finally synthesize through the filter. In this way, the steps of training, transformation, and synthesis can also be completed under the condition of limited training parameters to achieve the effect of improving sound quality.


## 4. Experimental Validation and Analysis

### 4.1. Discriminative Experiments for Mute/Clear/Turbid Tones (s/u/v)

The discriminant experiment of s/u/v was performed using a deep neural network approach. Experimental data were obtained from a laboratory-recorded telephone speech library collected in a municipal channel and quantified at a speech sampling rate of 8 kHz,16 bits. A total of 5000 frames of one speaker were selected with no overlap between long l0 ms, frames. Considering that the parameters change more than the silent segments, the number of three corpora is: 1000 frames, 2000 frames, and 2000 frames [10].


#### 4.1.1. Effect of the DNN Structure on the Results of Tough l Practice

The idea of normalization by feature dimension was adopted to the feature dimensions of all frames individually and the normalized function(2)x^i=xi−xminxmax−xmin.x^i and xi are the features of this dimension after and before normalization, respectively, xmax and xmin are the maximum and minimum values of this dimension feature, respectively. The deep neural network parameters are: batchsize (data group large) is 100, number of dnn—numepochs (hidden layer learning iterations) is 5, number of bp—numepochs (overall bp—numepochs (tuning iterations) is 20, and port (learning efficiency) is 1. The method used in the experiment is to count the i layer after the experiment, take the best structure (the structure in the top 10) as the front i layer structure of a hidden layer, and set the number of neurons in layer i + 1 to l 1200 to compare the identification rate; the experimental results are shown in Figure 1.Figure 1
Comparison of the neural network identification rates across each layer.The average recognition rate obtained from the best structural deep neural network of each layer of Figure1 shows that the neural network of a hidden layer, the traditional neural network, is generally recognized, which steadily increases as the hidden layer increases, but the five-layer deep neural network decreases in the recognition rate.


#### 4.1.2. Effect of the Parametric Features of the DNN on the Training Results

Deep neural network parameter settings are listed in Table1.Table 1
Deep neural network parameter settings.
Network topology1008080Batchsizes100a1dnn—numepochs5bp—numepochs100Here, a relatively random structure is selected as the experimental structure to test the change of the recognition rate under the number of features, and the experimental results are shown in Figure2. As seen from Figure 2(a), the rate of recognition increases to the steady point and increases faster as the number of training features increases. Figure 2(b) is the average of the recognition rate for the first 100 iterations of the 300–3,000-frame training features to represent the average recognition rate under the corresponding training conditions. It can be seen that as the number of training features increases, except for a slight decrease at 2100 to 2700 frames, however, the overall recognition rate is constantly increasing, and the decline may be less corpus features from 2100 to 2700 frames, causing some decline in the learning outcome performance of deep neural networks. Conclusion: increasing the number of training features can accelerate the convergence of deep neural network learning and improve the effect of deep neural network learning.Figure 2
Changes in the recognition rate under changes in the number of features.
(a)(b)


#### 4.1.3. The DNN Determines the s/u/v

All the 5000 frames corpus s/u/v is divided into 5 groups of 1000 frames. Three of the groups were randomly selected as the training corpus, and the other two as the test corpus, and five times. Parameter settings are listed in Table2, identification rate results are shown in Figure 3, and error rate statistics are listed in Table 3.Table 2
Deep neural network parameter settings.
Batchsizes100a1dnn—numepochs5bp—numepochs1–100Figure 3
The rate of discriminative recognition of s/u/v by the degree neural network varies with the number of iterations.Table 3
Deep neural network parameter settings.
Error rate (%)⟶s⟶u⟶vS—0.00980U0.0735—1.4985V02.9887—As is seen from Figure3, this deep neural network reaches a stable value after approximately 13 iterations, with a recognition rate of 98.3%. As can be seen from Table 3, the error rate between the mute and turbidity sounds is very low, while the error rate of noise recognition into the turbidity sound is about 1.5% and 3%, respectively. The reason may be that the pronunciation of some noise turbidity or some small energy turbidity segments itself is easily confused with each other, and the junction between turbidity and turbidity sounds cannot be completely accurately judged and may lead to some error in the annotation information. Overall, the experimental results demonstrate the effectiveness of deep neural networks for s/u/v discrimination [11].


### 4.2. Create and Set Up the Voice Broadcast Process

The creation and setting of the voice broadcast process is as follows:(1)
Select the speaker (e.g., zhangnan), and the background system sets the announcer to “zhangnan”; other relevant options are set to default.(2)
Choose voice libraries in the fields, such as telecom operators and railway and aviation industries.(3)
The operator can input the content of the broadcast through the keyboard and other input devices, or he can directly import the TXT text file.(4)
The operator can control the volume, audio, symbol reading, English reading, digital reading, and other options. Edit the content of the broadcast.(5)
As needed, insert the pre-recording (sound effects, music inserted before the start of the broadcast official content) and background sound (sound effects, music broadcast in sync with the broadcast official content).(6)
As required, you can choose the pronunciation style of the broadcasting, such as: cadence (applicable novel, comment, etc.) and stable end weight (applicable news, explanation, etc.).(7)
After editing, you can choose the broadcast (play) and output the broadcast through the audio equipment (sound system).(8)
The operator can save the broadcast project, choosing to file the output and call the next time you broadcast the same content.


### 4.3. Open the Voice Broadcast Process

The reservation voice broadcast process is as follows:(1)
Select “Appointment” and the system will automatically transfer to the appointment voice broadcast menu.(2)
In the reservation voice broadcast menu, open the file.(3)
Choose the appointment time, which can specifically refer to a certain time or a fixed time per day.(4)
Save the reservation voice broadcast.(5)
After the operation, the system will automatically start the voice broadcast task when the system time reaches the appointment time.


## 4.1. Discriminative Experiments for Mute/Clear/Turbid Tones (s/u/v)

The discriminant experiment of s/u/v was performed using a deep neural network approach. Experimental data were obtained from a laboratory-recorded telephone speech library collected in a municipal channel and quantified at a speech sampling rate of 8 kHz,16 bits. A total of 5000 frames of one speaker were selected with no overlap between long l0 ms, frames. Considering that the parameters change more than the silent segments, the number of three corpora is: 1000 frames, 2000 frames, and 2000 frames [10].


### 4.1.1. Effect of the DNN Structure on the Results of Tough l Practice

The idea of normalization by feature dimension was adopted to the feature dimensions of all frames individually and the normalized function(2)x^i=xi−xminxmax−xmin.x^i and xi are the features of this dimension after and before normalization, respectively, xmax and xmin are the maximum and minimum values of this dimension feature, respectively. The deep neural network parameters are: batchsize (data group large) is 100, number of dnn—numepochs (hidden layer learning iterations) is 5, number of bp—numepochs (overall bp—numepochs (tuning iterations) is 20, and port (learning efficiency) is 1. The method used in the experiment is to count the i layer after the experiment, take the best structure (the structure in the top 10) as the front i layer structure of a hidden layer, and set the number of neurons in layer i + 1 to l 1200 to compare the identification rate; the experimental results are shown in Figure 1.Figure 1
Comparison of the neural network identification rates across each layer.The average recognition rate obtained from the best structural deep neural network of each layer of Figure1 shows that the neural network of a hidden layer, the traditional neural network, is generally recognized, which steadily increases as the hidden layer increases, but the five-layer deep neural network decreases in the recognition rate.


### 4.1.2. Effect of the Parametric Features of the DNN on the Training Results

Deep neural network parameter settings are listed in Table1.Table 1
Deep neural network parameter settings.
Network topology1008080Batchsizes100a1dnn—numepochs5bp—numepochs100Here, a relatively random structure is selected as the experimental structure to test the change of the recognition rate under the number of features, and the experimental results are shown in Figure2. As seen from Figure 2(a), the rate of recognition increases to the steady point and increases faster as the number of training features increases. Figure 2(b) is the average of the recognition rate for the first 100 iterations of the 300–3,000-frame training features to represent the average recognition rate under the corresponding training conditions. It can be seen that as the number of training features increases, except for a slight decrease at 2100 to 2700 frames, however, the overall recognition rate is constantly increasing, and the decline may be less corpus features from 2100 to 2700 frames, causing some decline in the learning outcome performance of deep neural networks. Conclusion: increasing the number of training features can accelerate the convergence of deep neural network learning and improve the effect of deep neural network learning.Figure 2
Changes in the recognition rate under changes in the number of features.
(a)(b)


### 4.1.3. The DNN Determines the s/u/v

All the 5000 frames corpus s/u/v is divided into 5 groups of 1000 frames. Three of the groups were randomly selected as the training corpus, and the other two as the test corpus, and five times. Parameter settings are listed in Table2, identification rate results are shown in Figure 3, and error rate statistics are listed in Table 3.Table 2
Deep neural network parameter settings.
Batchsizes100a1dnn—numepochs5bp—numepochs1–100Figure 3
The rate of discriminative recognition of s/u/v by the degree neural network varies with the number of iterations.Table 3
Deep neural network parameter settings.
Error rate (%)⟶s⟶u⟶vS—0.00980U0.0735—1.4985V02.9887—As is seen from Figure3, this deep neural network reaches a stable value after approximately 13 iterations, with a recognition rate of 98.3%. As can be seen from Table 3, the error rate between the mute and turbidity sounds is very low, while the error rate of noise recognition into the turbidity sound is about 1.5% and 3%, respectively. The reason may be that the pronunciation of some noise turbidity or some small energy turbidity segments itself is easily confused with each other, and the junction between turbidity and turbidity sounds cannot be completely accurately judged and may lead to some error in the annotation information. Overall, the experimental results demonstrate the effectiveness of deep neural networks for s/u/v discrimination [11].


## 4.1.1. Effect of the DNN Structure on the Results of Tough l Practice

The idea of normalization by feature dimension was adopted to the feature dimensions of all frames individually and the normalized function(2)x^i=xi−xminxmax−xmin.x^i and xi are the features of this dimension after and before normalization, respectively, xmax and xmin are the maximum and minimum values of this dimension feature, respectively. The deep neural network parameters are: batchsize (data group large) is 100, number of dnn—numepochs (hidden layer learning iterations) is 5, number of bp—numepochs (overall bp—numepochs (tuning iterations) is 20, and port (learning efficiency) is 1. The method used in the experiment is to count the i layer after the experiment, take the best structure (the structure in the top 10) as the front i layer structure of a hidden layer, and set the number of neurons in layer i + 1 to l 1200 to compare the identification rate; the experimental results are shown in Figure 1.Figure 1
Comparison of the neural network identification rates across each layer.The average recognition rate obtained from the best structural deep neural network of each layer of Figure1 shows that the neural network of a hidden layer, the traditional neural network, is generally recognized, which steadily increases as the hidden layer increases, but the five-layer deep neural network decreases in the recognition rate.


## 4.1.2. Effect of the Parametric Features of the DNN on the Training Results

Deep neural network parameter settings are listed in Table1.Table 1
Deep neural network parameter settings.
Network topology1008080Batchsizes100a1dnn—numepochs5bp—numepochs100Here, a relatively random structure is selected as the experimental structure to test the change of the recognition rate under the number of features, and the experimental results are shown in Figure2. As seen from Figure 2(a), the rate of recognition increases to the steady point and increases faster as the number of training features increases. Figure 2(b) is the average of the recognition rate for the first 100 iterations of the 300–3,000-frame training features to represent the average recognition rate under the corresponding training conditions. It can be seen that as the number of training features increases, except for a slight decrease at 2100 to 2700 frames, however, the overall recognition rate is constantly increasing, and the decline may be less corpus features from 2100 to 2700 frames, causing some decline in the learning outcome performance of deep neural networks. Conclusion: increasing the number of training features can accelerate the convergence of deep neural network learning and improve the effect of deep neural network learning.Figure 2
Changes in the recognition rate under changes in the number of features.
(a)(b)


## 4.1.3. The DNN Determines the s/u/v

All the 5000 frames corpus s/u/v is divided into 5 groups of 1000 frames. Three of the groups were randomly selected as the training corpus, and the other two as the test corpus, and five times. Parameter settings are listed in Table2, identification rate results are shown in Figure 3, and error rate statistics are listed in Table 3.Table 2
Deep neural network parameter settings.
Batchsizes100a1dnn—numepochs5bp—numepochs1–100Figure 3
The rate of discriminative recognition of s/u/v by the degree neural network varies with the number of iterations.Table 3
Deep neural network parameter settings.
Error rate (%)⟶s⟶u⟶vS—0.00980U0.0735—1.4985V02.9887—As is seen from Figure3, this deep neural network reaches a stable value after approximately 13 iterations, with a recognition rate of 98.3%. As can be seen from Table 3, the error rate between the mute and turbidity sounds is very low, while the error rate of noise recognition into the turbidity sound is about 1.5% and 3%, respectively. The reason may be that the pronunciation of some noise turbidity or some small energy turbidity segments itself is easily confused with each other, and the junction between turbidity and turbidity sounds cannot be completely accurately judged and may lead to some error in the annotation information. Overall, the experimental results demonstrate the effectiveness of deep neural networks for s/u/v discrimination [11].


## 4.2. Create and Set Up the Voice Broadcast Process

The creation and setting of the voice broadcast process is as follows:(1)
Select the speaker (e.g., zhangnan), and the background system sets the announcer to “zhangnan”; other relevant options are set to default.(2)
Choose voice libraries in the fields, such as telecom operators and railway and aviation industries.(3)
The operator can input the content of the broadcast through the keyboard and other input devices, or he can directly import the TXT text file.(4)
The operator can control the volume, audio, symbol reading, English reading, digital reading, and other options. Edit the content of the broadcast.(5)
As needed, insert the pre-recording (sound effects, music inserted before the start of the broadcast official content) and background sound (sound effects, music broadcast in sync with the broadcast official content).(6)
As required, you can choose the pronunciation style of the broadcasting, such as: cadence (applicable novel, comment, etc.) and stable end weight (applicable news, explanation, etc.).(7)
After editing, you can choose the broadcast (play) and output the broadcast through the audio equipment (sound system).(8)
The operator can save the broadcast project, choosing to file the output and call the next time you broadcast the same content.


## 4.3. Open the Voice Broadcast Process

The reservation voice broadcast process is as follows:(1)
Select “Appointment” and the system will automatically transfer to the appointment voice broadcast menu.(2)
In the reservation voice broadcast menu, open the file.(3)
Choose the appointment time, which can specifically refer to a certain time or a fixed time per day.(4)
Save the reservation voice broadcast.(5)
After the operation, the system will automatically start the voice broadcast task when the system time reaches the appointment time.


## 5. Conclusions

Speech synthesis technology has many advantages in the voice broadcasting application, such as the development form is simple, the voice library has traffic, etc., and in the basic corpus of the corresponding industry broadcasting personnel of the field library, such voice synthesis voice engine mode can be applied to telecommunications, railway, and banking industries; intelligent voice broadcast services have a wide application demand and good development prospects; for the current competitive telecom operators to improve their business hall service level, creating voice broadcast brands provides a good choice.



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*Source: 1971679-2022-06-23.xml*