• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3992-3998
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• 2013

This paper is related to the method of adding a emotional speech corpus to a high-quality large corpus based speech synthesizer, and generating various synthesized speech. We made the emotional speech corpus as a form which can be used in waveform concatenated speech synthesizer, and have implemented the speech synthesizer that can be generated various synthesized speech through the same synthetic unit selection process of normal speech synthesizer. We used a markup language for emotional input text. Emotional speech is generated when the input text is matched as much as the length of intonation phrase in emotional speech corpus, but in the other case normal speech is generated. The BIs(Break Index) of emotional speech is more irregular than normal speech. Therefore, it becomes difficult to use the BIs generated in a synthesizer as it is. In order to solve this problem we applied the Variable Break[3] modeling. We used the Japanese speech synthesizer for experiment. As a result we obtained the natural emotional synthesized speech using the break prediction module for normal speech synthesize.

• Speech Sciences
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• v.14 no.2
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• pp.91-103
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• 2007

The purpose of this paper is to develop a model enabling to predict the prosody of Korean text-to-speech synthesis using the CART and SKES algorithms. CART prefers a prediction variable in many instances. Therefore, a partition method by F-Test was applied to CART which had reduced the number of instances by grouping phonemes. Furthermore, the quality of the text-to-speech synthesis was evaluated after applying the SKES algorithm to the same data size. For the evaluation, MOS tests were performed on 30 men and women in their twenties. Results showed that the synthesized speech was improved in a more clear and natural manner by applying the SKES algorithm.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.469-475
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• 2022

To train the model of the deep learning-based single-speaker TTS system, a speech DB of tens of hours and a lot of training time are required. This is an inefficient method in terms of time and cost to train multi-speaker or personalized TTS models. The voice cloning method uses a speaker encoder model to make the TTS model of a new speaker. Through the trained speaker encoder model, a speaker embedding vector representing the timbre of the new speaker is created from the small speech data of the new speaker that is not used for training. In this paper, we propose a multi-speaker TTS system to which voice cloning is applied. The proposed TTS system consists of a speaker encoder, synthesizer and vocoder. The speaker encoder applies the d-vector technique used in the speaker recognition field. The timbre of the new speaker is expressed by adding the d-vector derived from the trained speaker encoder as an input to the synthesizer. It can be seen that the performance of the proposed TTS system is excellent from the experimental results derived by the MOS and timbre similarity listening tests.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5763-5768
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• 2014

Maintaining a voice color is important when compounding both the normal voice because an emotion is not expressed with various emotional voices in a single synthesizer. When a synthesizer is developed using the recording data of too many expressed emotions, a voice color cannot be maintained and each synthetic speech is can be heard like the voice of different speakers. In this paper, the speech data was recorded and the change in the voice color was analyzed to develop an emotional HMM-based speech synthesizer. To realize a speech synthesizer, a voice was recorded, and a database was built. On the other hand, a recording process is very important, particularly when realizing an emotional speech synthesizer. Monitoring is needed because it is quite difficult to define emotion and maintain a particular level. In the realized synthesizer, a normal voice and three emotional voice (Happiness, Sadness, Anger) were used, and each emotional voice consists of two levels, High/Low. To analyze the voice color of the normal voice and emotional voice, the average spectrum, which was the measured accumulated spectrum of vowels, was used and the F1(first formant) calculated by the average spectrum was compared. The voice similarity of Low-level emotional data was higher than High-level emotional data, and the proposed method can be monitored by the change in voice similarity.

• Speech Sciences
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• v.7 no.2
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• pp.193-208
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• 2000

This paper describes a multi-step method of intonation modeling for corpus-based Korean speech synthesizer. We selected 1833 sentences considering various syntactic structures and built a corresponding speech corpus uttered by a female announcer. We detected the pitch using laryngograph signals and manually marked the prosodic boundaries on recorded speech, and carried out the tagging of part-of-speech and syntactic analysis on the text. The detected pitch was separated into 3 frequency bands of low, mid, high frequency components which correspond to the baseline, the word tone, and the syllable tone. We predicted them using the CART method and the Viterbi search algorithm with a word-tone-dictionary. In the collected spoken sentences, 1500 sentences were trained and 333 sentences were tested. In the layer of word tone modeling, we compared two methods. One is to predict the word tone corresponding to the mid-frequency components directly and the other is to predict it by multiplying the ratio of the word tone to the baseline by the baseline. The former method resulted in a mean error of 12.37 Hz and the latter in one of 12.41 Hz, similar to each other. In the layer of syllable tone modeling, it resulted in a mean error rate less than 8.3% comparing with the mean pitch, 193.56 Hz of the announcer, so its performance was relatively good.

• Speech Sciences
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• v.13 no.4
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• pp.187-200
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• 2006

The speech synthesis technology is widely used and its application area is also being broadened to an automatic response service, a learning system for handicapped person, etc. However, the sound quality of the speech synthesizer has not yet reached to the satisfactory level of users. To make a synthesized speech, the existing synthesizer generates rhythms only by the interval information such as space and comma or by several punctuation marks such as a question mark and an exclamation mark so that it is not easy to generate natural rhythms of people even though it is based on mass speech database. To make up for the problem, there is a way to select rhythms after processing language from a higher level information. This paper proposes a method for generating tags for controling rhythms by analyzing the meaning of sentence with speech situation information. We use the Systemic Functional Grammar (SFG) [4] which analyzes the meaning of sentence with speech situation information considering the sentence prior to the given one, the situation of a conversation, the relationship among people in the conversation, etc. In this study, we generate Semantic Speech Control Tag (SSCT) by the result of SFG's meaning analysis and the voice wave analysis.

• The Journal of the Acoustical Society of Korea
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• v.24 no.7
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• pp.365-370
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• 2005

The present study is purposed to establish a systematic theory for user-friendly approach to the creation of using a programming language using Csound. Csound is a world-wide computer music programming language and a software synthesizer specialized for prominent sound designers developed by Barry Vercoe at the Media Laboratory in M.I.T. The introduction and the main body of this paper suggested as the starting point of creating electronic music and musical sound the time of combination of music with natural sound or sound from specific media from the viewpoint of traditional Western music. and presents a systematic method composed of the principle of the operation of Csound and basic data samples.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.703-710
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• 2003

Because of their human-like synthesized speech quality, recently Corpus-Based Text-To-Speech(CB-TTS) have been actively studied worldwide. However, due to their large size speech database (DB), their application is very restricted. In this paper we propose and evaluate three DB reduction algorithms to which are designed to solve the above drawback. The first method is based on a K-means clustering approach, which selects k-representatives among multiple instances. The second method is keeping only those unit instances that are selected during synthesis, using a domain-restricted text as input to the synthesizer. The third method is a kind of hybrid approach of the above two methods and is using a large text as input in the system. After synthesizing the given sentences, the used unit instances and their occurrence information is extracted. As next step a modified K-means clustering is applied, which takes into account also the occurrence information of the selected unit instances, Finally we compare three pruning methods by evaluating the synthesized speech quality for the similar DB reduction rate, Based on perceptual listening tests, we concluded that the last method shows the best performance among three algorithms. More than this, the results show that the last method is able to reduce DB size without speech quality looses.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.209-215
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• 2010

Recently portable terminal is received attention by wireless networks and mass capacity ROM. In this result, TTS(Text to Speech) system is inserted to portable terminal. Nevertheless high quality synthesis is difficult in portable terminal, users need high quality synthesis. In this paper, we proposed Distributed TTS (DTTS) that was composed of server and terminal. The DTTS on corpus based speech synthesis can be high quality synthesis. Synthesis system in server that generate optimized speech concatenation information after database search and transmit terminal. Synthesis system in terminal make high quality speech synthesis as low computation using transmitted speech concatenation information from server. The proposed method that can be reducing complexity, smaller power consumption and efficient maintenance.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.53-63
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• 2015

Singing voice synthesis is the generation of a song using a computer given its lyrics and musical notes. Hidden Markov models (HMM) have been proved to be the models of choice for text to speech synthesis. HMMs have also been used for singing voice synthesis research, however, a huge database is needed for the training of HMMs for singing voice synthesis. And commercially available singing voice synthesis systems which use the piano roll music notation, needs to adopt the easy to read standard music notation which make it suitable for singing learning applications. To overcome this problem, we use a speech database for training context dependent HMMs, to be used for singing voice synthesis. Pitch and duration control methods have been devised to modify the parameters of the HMMs trained on speech, to be used as the synthesis units for the singing voice. This work describes a singing voice synthesis system which uses a MusicXML based music score editor as the front-end interface for entry of the notes and lyrics to be synthesized and a hidden Markov model based text to speech synthesis system as the back-end synthesizer. A perceptual test shows the feasibility of our proposed system.