• Proceedings of the KSPS conference
• /
• 2002.11a
• /
• pp.33-36
• /
• 2002

As the telematics service that is the integration of information technology approaches commercialization, the necessity and gravity of speech technology is rapidly growing. The speech technology occupies important position in the telematics service because it informs the starting of service and the retrieved result. This service must provide high accuracy of speech recognition and natural synthesis of human speech in a driving environment and it is especially true for the fee-for-service. For high quality TTS, the speech synthesis technique that makes optimal synthesis database and uses efficiently this database is required. In this paper, we describe the design of phonetically balanced sentences used for speech database, the selection of service-suitable-speaker, the extraction methods of accurate phoneme boundary, and the factors which are taken into consideration in the extraction stage of prosody. Finally we show the real case that has commercially implemented.

• Speech Sciences
• /
• v.10 no.1
• /
• pp.85-94
• /
• 2003

In this paper an algorithm to reduce the size of Text-to-Speech database is proposed. The algorithm is based on the characteristics of Korean phonemic units. From the initial database, a reduced phoneme unit set is induced by articulatory similarity of concatenating phonemes. Speech data is read by one female announcer for 1000 phonetically balanced sentences. All the recorded speech is then segmented by phoneticians. Total size of the original speech data is about 640 MB including laryngograph signal. To synthesize wave, RE-PSOLA (Residual-Excited Pitch Synchronous Overlap and Add Method) was used. The voice quality of synthesized speech was compared with original speech in terms of spectrographic informations and objective tests. The quality of the synthesized speech is not much degraded when the size of synthesis DB was reduced from 320 MB to 82 MB.

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.3
• /
• pp.64-72
• /
• 1997

In this paper, study and experiments are performed for finding recognition unit fit which can be used in large vocabulary recognition system. Specifically, a phoneme that is currently used as recognition unit and a syllable in which Korean is well characterized are selected. From comparisons of recognition experiments, the study is performed whether a syllable can be considered as recognition unit of Korean recognition system. For report of an objective result of the comparison experiment, we collected speech data of a male speaker and processed them by hand-segmentation for phoneme boundary and labeling to construct speech database. And for training and recognition based on HMM, we used HTK (HMM Tool Kit) 2.0 of commercial tool from Entropic Co. to experiment in same condition. We applied two HMM model topologies, 3 emitting state of 5 state and 6 emitting state of 8 state, in Continuous HMM on training of each recognition unit. We also used 3 sets of PBW (Phonetically Balanced Words) and 1 set of POW(Phonetically Optimized Words) for training and another 1 set of PBW for recognition, that is "Speaker Dependent Medium Vocabulary Size Recognition." Experiments result reports that recognition rate is 95.65% in phoneme unit, 94.41% in syllable unit and decoding time of recognition in syllable unit is faster by 25% than in phoneme.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.1
• /
• pp.48-53
• /
• 2003

Eigenvoice method is known to be adequate for fast speaker adaptation, but it hardly shows additional improvement with increased amount of adaptation data. In this paper, to deal with this problem, we propose a modified method estimating the weights of eigenvoices in each feature vector dimension. We also propose an adaptation mode selection scheme that one method with higher performance among several adaptation methods is selected according to the amount of adaptation data. We used POW DB to construct the speaker independent model and eigenvoices, and utterances(ranging from 1 to 50) from PBW 452 DB and the remaining 400 utterances were used for adaptation and evaluation, respectively. With the increased amount of adaptation data, proposed dimensional eigenvoice method showed higher performance than both conventional eigenvoice method and MLLR. Up to 26% of word error rate was reduced by the adaptation mode selection between eigenvoice and dimensional eigenvoice methods in comparison with conventional eigenvoice method.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.2
• /
• pp.27-34
• /
• 2001

Utterance verification is used in variable vocabulary word recognition to reject the word that does not belong to in-vocabulary word or does not belong to correctly recognized word. Utterance verification is an important technology to design a user-friendly speech recognition system. We propose a new utterance verification algorithm for no-training utterance verification system based on the minimum verification error. First, using PBW (Phonetically Balanced Words) DB (445 words), we create no-training anti-phoneme models which include many PLUs(Phoneme Like Units), so anti-phoneme models have the minimum verification error. Then, for OOV (Out-Of-Vocabulary) rejection, the phoneme-based confidence measure which uses the likelihood between phoneme model (null hypothesis) and anti-phoneme model (alternative hypothesis) is normalized by null hypothesis, so the phoneme-based confidence measure tends to be more robust to OOV rejection. And, the word-based confidence measure which uses the phoneme-based confidence measure has been shown to provide improved detection of near-misses in speech recognition as well as better discrimination between in-vocabularys and OOVs. Using our proposed anti-model and confidence measure, we achieve significant performance improvement; CA (Correctly Accept for In-Vocabulary) is about 89％, and CR (Correctly Reject for OOV) is about 90％, improving about 15-21％ in ERR (Error Reduction Rate).

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.4
• /
• pp.35-41
• /
• 1997

Speech database is an indispensable part of speech research. Speech database is necessary to use in speech research and development processes, and to evaluate performances of various speech-processing systems. To use speech database for common purpose, it is necessary to design utterance list that has all the possible phonetical events in minimal number of words, and is independent of tasks. To meet those restrictions this paper extracts PBW set from large text corpus. Speech database that was constructed using PBW set for utterance list and its properties are described in this paper.

• The Journal of the Acoustical Society of Korea
• /
• v.18 no.3
• /
• pp.17-26
• /
• 1999

We present the first speech coarticulation database of Korean, English and Konglish/sup 3)/ named "SORIDA"/sup 4)/, which is designed to cover the maximum number of representations of coarticulation in these languages [1]. SORIDA features a compact database which is designed to contain a maximum number of triphones in a minimum number of prompts. SORIDA contains all consonantal triphones and vowel allophones in 682 Korean prompts of word length and in 717 English prompt words, spoken five times by speakers of balanced genders, dialects and ages. Korean prompts are synthesized lexicons which maximize their coarticulation variation disregarding any stress phenomena, while English prompts are natural words that fully reflect their stress effects with respect to the coarticulation variation. The prompts are designed differently because English phonology has stress while Korean does not. An intermediate language, Konglish has also been modeled by two Korean speakers reading 717 English prompt words. Recording was done in a controlled laboratory environment with an AKG Model C-100 microphone and a Fostex D-5 digital-audio-tape (DAT) recorder. The total recording time lasted four hours. SORIDA CD-ROM is available in one disk of 22.05 kHz sampling rate with a 16 bit sample size. SORIDA digital audio-tapes are available in four 124-minute-tapes of 48 kHz sampling rate. SORIDA′s list of phonetically-rich-words is also available in English and Korean.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.2E
• /
• pp.43-50
• /
• 2009

To get more natural synthetic speech generated by a Korean TTS (Text-To-Speech) system, we have to know all the possible prosodic rules in Korean spoken language. We should find out these rules from linguistic, phonetic information or from real speech. In general, all of these rules should be integrated into a prosody-generation algorithm in a TTS system. But this algorithm cannot cover up all the possible prosodic rules in a language and it is not perfect, so the naturalness of synthesized speech cannot be as good as we expect. ANNs (Artificial Neural Networks) can be trained to learn the prosodic rules in Korean spoken language. To train and test ANNs, we need to prepare the prosodic patterns of all the phonemic segments in a prosodic corpus. A prosodic corpus will include meaningful sentences to represent all the possible prosodic rules. Sentences in the corpus were made by picking up a series of words from the list of PB (phonetically Balanced) isolated words. These sentences in the corpus were read by speakers, recorded, and collected as a speech database. By analyzing recorded real speech, we can extract prosodic pattern about each phoneme, and assign them as target and test patterns for ANNs. ANNs can learn the prosody from natural speech and generate prosodic patterns of the central phonemic segment in phoneme strings as output response of ANNs when phoneme strings of a sentence are given to ANNs as input stimuli.

• The Journal of the Acoustical Society of Korea
• /
• v.9 no.4
• /
• pp.66-76
• /
• 1990

In this work, the effect of noise and distortion of a telephone channel on the speech recognition is studied, and methods to improve the recognition rate are proposed. Computer simulation is done using the 100-word test data whichwere made by pronouncing ten times 100-phonetically balanced Korean isolated words in a speaker dependent mode. First, a spectral subtraction method is suggested to improve the noisy speech recognition. Then, the effect of bandwidth limiting and channel distortion is studied. It has been found that bandwidth limiting and amplitude distortion lower the recognition rate significantly, but phase distortion affects little. To reduce the channel effect, we modify the reference pattern according to some training data. When both channel noise and distortion exist, the recognition rate without the proposed method is merely 7.7~26.4%, but the recognition rate with the proposed method is drastically increased to 76.2~92.3%.

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.6
• /
• pp.106-109
• /
• 1997

For the practical isolated word recognition system, the ability to reject the out-of -vocabulary(OOV) is required. In this paper, we present a rejection method which uses the clustered phoneme modeling combined with postprocessing by likelihood ratio scoring. Our baseline speech recognition system was based on the whole-word continuous HMM. And 6 clustered phoneme models were generated using statistical method from the 45 context independent phoneme models, which were trained using the phonetically balanced speech database. The test of the rejection performance for speaker independent isolated words recogntion task on the 22 section names shows that our method is superior to the conventional postprocessing method, performing the rejection according to the likelihood difference between the first and second candidates. Furthermore, this clustered phoneme models do not require retraining for the other isolated word recognition system with different vocabulary sets.