• Title/Summary/Keyword: Part of speech

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Vocal Tract Area Estimation from Deaf and Normal Children's Speech (청각장애아 및 건청아 음성으로부터 성도 면적 추정)

  • Kim, Se-Hwan;Kwon, Oh-Wook
    • Proceedings of the KSPS conference
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    • 2005.11a
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    • pp.51-54
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    • 2005
  • This paper analyzes the vocal tract area estimation algorithm used as a part of a speech analysis program to help deaf children correct their pronunciations by comparing their vocal tract shape with normal children's. Assuming that a vocal tract is a concatenation of cylinder tubes with a different cross section, we compute the relative vocal tract area of each tube using the reflection coefficients obtained from linear predictive coding. Then, obtain the absolute vocal tract area by computing the height of lip opening with a formula modified for children's speech. Using the speech data for five Korean vowels (/a/, /e/, /i/, /o/, and /u/), we investigate the effects of the sampling frequency, frame size, and model order. We compare vocal tract shapes obtained from deaf and normal children's speech.

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Performance of Vocal Tract Area Estimation from Deaf and Normal Children's Speech (청각장애아동과 건청아동의 성도면적 추정 성능)

  • Kim Se-Hwan;Kim Nam;Kwon Oh-Wook
    • MALSORI
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    • no.56
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    • pp.159-172
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    • 2005
  • This paper analyzes the vocal tract area estimation algorithm used as a part of a speech analysis program to help deaf children correct their pronunciations by comparing their vocal tract shape with normal children's. Assuming that a vocal tract is a concatenation of cylinder tubes with a different cross section, we compute the relative vocal tract area of each tube using the reflection coefficients obtained from linear predictive coding. Then, we obtain the absolute vocal tract area by computing the height of lip opening with a formula modified for children's speech. Using the speech data for five Korean vowels (/a/, /e/, /i/, /o/, and /u/), we investigate the effects of the sampling frequency, frame size, and model order on the estimated vocal tract shape. We compare the vocal tract shapes obtained from deaf and normal children's speech.

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Prediction of Prosodic Boundary Strength by means of Three POS(Part of Speech) sets (품사셋에 의한 운율경계강도의 예측)

  • Eom Ki-Wan;Kim Jin-Yeong;Kim Seon-Mi;Lee Hyeon-Bok
    • MALSORI
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    • no.35_36
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    • pp.145-155
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    • 1998
  • This study intended to determine the most appropriate POS(Part of Speech) sets for predicting prosodic boundary strength efficiently. We used 3-level POB bets which Kim(1997), one of the authors, has devised. Three POS sets differ from each other according to how much grammatical information they have: the first set has maximal syntactic and morphological information which possibly affects prosodic phrasing, and the third set has minimal one. We hand-labelled 150 sentences using each of three POS sets and conducted perception test. Based on the results of the test, stochastic language modeling method was used to predict prosodic boundary strength. The results showed that the use of each POS set led to not too much different efficiency in the prediction, but the second set was a little more efficient than the other two. As far as the complexity in stochastic language modeling is concerned, however, the third set may be also preferable.

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Study about Windows System Control Using Gesture and Speech Recognition (제스처 및 음성 인식을 이용한 윈도우 시스템 제어에 관한 연구)

  • 김주홍;진성일이남호이용범
    • Proceedings of the IEEK Conference
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    • 1998.10a
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    • pp.1289-1292
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    • 1998
  • HCI(human computer interface) technologies have been often implemented using mouse, keyboard and joystick. Because mouse and keyboard are used only in limited situation, More natural HCI methods such as speech based method and gesture based method recently attract wide attention. In this paper, we present multi-modal input system to control Windows system for practical use of multi-media computer. Our multi-modal input system consists of three parts. First one is virtual-hand mouse part. This part is to replace mouse control with a set of gestures. Second one is Windows control system using speech recognition. Third one is Windows control system using gesture recognition. We introduce neural network and HMM methods to recognize speeches and gestures. The results of three parts interface directly to CPU and through Windows.

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Relationship between Speech Perception in Noise and Phonemic Restoration of Speech in Noise in Individuals with Normal Hearing

  • Vijayasarathy, Srikar;Barman, Animesh
    • Journal of Audiology & Otology
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    • v.24 no.4
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    • pp.167-173
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    • 2020
  • Background and Objectives: Top-down restoration of distorted speech, tapped as phonemic restoration of speech in noise, maybe a useful tool to understand robustness of perception in adverse listening situations. However, the relationship between phonemic restoration and speech perception in noise is not empirically clear. Subjects and Methods: 20 adults (40-55 years) with normal audiometric findings were part of the study. Sentence perception in noise performance was studied with various signal-to-noise ratios (SNRs) to estimate the SNR with 50% score. Performance was also measured for sentences interrupted with silence and for those interrupted by speech noise at -10, -5, 0, and 5 dB SNRs. The performance score in the noise interruption condition was subtracted by quiet interruption condition to determine the phonemic restoration magnitude. Results: Fairly robust improvements in speech intelligibility was found when the sentences were interrupted with speech noise instead of silence. Improvement with increasing noise levels was non-monotonic and reached a maximum at -10 dB SNR. Significant correlation between speech perception in noise performance and phonemic restoration of sentences interrupted with -10 dB SNR speech noise was found. Conclusions: It is possible that perception of speech in noise is associated with top-down processing of speech, tapped as phonemic restoration of interrupted speech. More research with a larger sample size is indicated since the restoration is affected by the type of speech material and noise used, age, working memory, and linguistic proficiency, and has a large individual variability.

Relationship between Speech Perception in Noise and Phonemic Restoration of Speech in Noise in Individuals with Normal Hearing

  • Vijayasarathy, Srikar;Barman, Animesh
    • Korean Journal of Audiology
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    • v.24 no.4
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    • pp.167-173
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    • 2020
  • Background and Objectives: Top-down restoration of distorted speech, tapped as phonemic restoration of speech in noise, maybe a useful tool to understand robustness of perception in adverse listening situations. However, the relationship between phonemic restoration and speech perception in noise is not empirically clear. Subjects and Methods: 20 adults (40-55 years) with normal audiometric findings were part of the study. Sentence perception in noise performance was studied with various signal-to-noise ratios (SNRs) to estimate the SNR with 50% score. Performance was also measured for sentences interrupted with silence and for those interrupted by speech noise at -10, -5, 0, and 5 dB SNRs. The performance score in the noise interruption condition was subtracted by quiet interruption condition to determine the phonemic restoration magnitude. Results: Fairly robust improvements in speech intelligibility was found when the sentences were interrupted with speech noise instead of silence. Improvement with increasing noise levels was non-monotonic and reached a maximum at -10 dB SNR. Significant correlation between speech perception in noise performance and phonemic restoration of sentences interrupted with -10 dB SNR speech noise was found. Conclusions: It is possible that perception of speech in noise is associated with top-down processing of speech, tapped as phonemic restoration of interrupted speech. More research with a larger sample size is indicated since the restoration is affected by the type of speech material and noise used, age, working memory, and linguistic proficiency, and has a large individual variability.

Text to Speech System from Web Images (웹상의 영상 내의 문자 인식과 음성 전환 시스템)

  • 안희임;정기철
    • Proceedings of the IEEK Conference
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    • 2001.06c
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    • pp.5-8
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    • 2001
  • The computer programs based upon graphic user interface(GUI) became commonplace with the advance of computer technology. Nevertheless, programs for the visually-handicapped have still remained at the level of TTS(text to speech) programs and this prevents many visually-handicapped from enjoying the pleasure and convenience of the information age. This paper is, paying attention to the importance of character recognition in images, about the configuration of the system that converts text in the image selected by a user to the speech by extracting the character part, and carrying out character recognition.

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A Study on Endpoint Detection and Syllable Segmentation System Using Ramp Edge Detection (Ramp Edge Detection을 이용한 끝점 검출과 음절 분할에 관한 연구)

  • 유일수;홍광석
    • Proceedings of the IEEK Conference
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    • 2003.07e
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    • pp.2216-2219
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    • 2003
  • Accurate speech region detection and automatic syllable segmentation is important part of speech recognition system. In automatic speech recognition system, they are needed for the purpose of accurate recognition and less computational complexity, In this paper, we Propose improved syllable segmentation method using ramp edge detection method and residual signal Peak energy. These methods were used to ensure accuracy and robustness for endpoint detection and syllable segmentation system. They have almost invariant response to various background noise levels. As experimental results, we obtained the rate of 90.7% accuracy in syllable segmentation in a condition of accurate endpoint detection environments.

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A 4800 BPS LPS Vocoder with Improved Exitation (개선된 여기신호의 4800BPS LPC 보코우터)

  • 은종관;성원용
    • The Journal of the Acoustical Society of Korea
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    • v.1 no.1
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    • pp.54-59
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    • 1982
  • We present an improved 4800 bps LPC vocoder system that virtually eleminates the buzzy effect from synthetic speech. Excitation signal in the new system is formed by adding high-pass filtered pitch pulses or random noise to a baseband residual signal that has been coded by pitch predictive PCM. Since the baseband residual is used as a part of excitation, the system is also robust to V/UV and pitch errors. According to our informal listening tests, the synthetic speech of the new system does not have the buzzy effect. As a result the vocoder speech quality is more natural than that of a conventioinal LPC vocoder.

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A Parser of Definitions in Korean Dictionary based on Probabilistic Grammar Rules (확률적 문법규칙에 기반한 국어사전의 뜻풀이말 구문분석기)

  • Lee, Su Gwang;Ok, Cheol Yeong
    • Journal of KIISE:Software and Applications
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    • v.28 no.5
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    • pp.448-448
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    • 2001
  • The definitions in Korean dictionary not only describe meanings of title, but also include various semantic information such as hypernymy/hyponymy, meronymy/holonymy, polysemy, homonymy, synonymy, antonymy, and semantic features. This paper purposes to implement a parser as the basic tool to acquire automatically the semantic information from the definitions in Korean dictionary. For this purpose, first we constructed the part-of-speech tagged corpus and the tree tagged corpus from the definitions in Korean dictionary. And then we automatically extracted from the corpora the frequency of words which are ambiguous in part-of-speech tag and the grammar rules and their probability based on the statistical method. The parser is a kind of the probabilistic chart parser that uses the extracted data. The frequency of words which are ambiguous in part-of-speech tag and the grammar rules and their probability resolve the noun phrase's structural ambiguity during parsing. The parser uses a grammar factoring, Best-First search, and Viterbi search In order to reduce the number of nodes during parsing and to increase the performance. We experiment with grammar rule's probability, left-to-right parsing, and left-first search. By the experiments, when the parser uses grammar rule's probability and left-first search simultaneously, the result of parsing is most accurate and the recall is 51.74% and the precision is 87.47% on raw corpus.