• Archives of Plastic Surgery
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• v.41 no.5
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• pp.472-479
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• 2014

Background Speech problems are a common clinical feature of the 22q11.2 deletion syndrome. The objectives of this study were to inventory the speech history and current self-reported speech rating of adolescents and young adults, and examine the possible variables influencing the current speech ratings, including cleft palate, surgery, speech and language therapy, intelligence quotient, and age at assessment. Methods In this cross-sectional cohort study, 50 adolescents and young adults with the 22q11.2 deletion syndrome (ages, 12-26 years, 67% female) filled out questionnaires. A neuropsychologist administered an age-appropriate intelligence quotient test. The demographics, histories, and intelligence of patients with normal speech (speech rating=1) were compared to those of patients with different speech (speech rating>1). Results Of the 50 patients, a minority (26%) had a cleft palate, nearly half (46%) underwent a pharyngoplasty, and all (100%) had speech and language therapy. Poorer speech ratings were correlated with more years of speech and language therapy (Spearman's correlation=0.418, P=0.004; 95% confidence interval, 0.145-0.632). Only 34% had normal speech ratings. The groups with normal and different speech were not significantly different with respect to the demographic variables; a history of cleft palate, surgery, or speech and language therapy; and the intelligence quotient. Conclusions All adolescents and young adults with the 22q11.2 deletion syndrome had undergone speech and language therapy, and nearly half of them underwent pharyngoplasty. Only 34% attained normal speech ratings. Those with poorer speech ratings had speech and language therapy for more years.

• Speech Sciences
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• v.9 no.1
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• pp.49-59
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• 2002

This paper concerns an effective text-dependent speaker verification method to increase the performance of speaker verification. While various speaker verification methods have already been developed, their effectiveness has not yet been formally proven in terms of achieving acceptable performance levels. This paper proposes a weighted likelihood procedure along with a confidence measure based on subword-based text-dependent speaker verification. Our aim is to remedy the low performance problem in speaker verification by exploring a means to strengthen the verification likelihood via subword-based hypothesis criteria and weighted likelihood method. Experimental results show that the proposed speaker verification method outperforms that of the speaker verification scheme without using the proposed decision by a factor of up to 1.6 times. From these results, the proposed speaker verification method is shown to be very effective and to achieve a reliable performance.

• MALSORI
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• no.46
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• pp.103-115
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• 2003

In this paper, we proposed Background Model Set algorithm used in the speaker verification to improve calculating confidence measure(CM) in speech recognition. CM is to display relative likelihood between recognized models and antiphone models. In previous method calculating of CM, we calculated probability and standard deviation using all phonemes in composition of antiphone models. At this process, antiphone CM brought bad recognition result. Also, recognition time increases. In order to solve this problem, we studied about method to reconstitute average and standard deviation using BMS algorithm in CM calculation.

• Phonetics and Speech Sciences
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• v.1 no.1
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• pp.55-62
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• 2009

This paper proposes an improved method to verify a keyword segment that results from a word spotting system. First a baseline word spotting system is implemented. In order to improve performance of the word spotting systems, we use a two-pass structure which consists of a word spotting system and an utterance verification system. Using the basic likelihood ratio test (LRT) based utterance verification system to verify the keywords, there have been certain problems which lead to performance degradation. So, we propose a method which uses phone-level log-likelihood ratios (PLLR) patterns in computing confidence measures for each keyword. The proposed method generates weights according to the PLLR patterns and assigns different weights to each phone in the process of generating confidence measures for the keywords. This proposed method has shown to be more appropriate to word spotting systems and we can achieve improvement in final word spotting accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.755-759
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• 2009

The ability to recognize human emotion is one of the hallmarks of human-robot interaction. Especially, speaker-independent emotion recognition is a challenging issue for commercial use of speech emotion recognition systems. In general, speaker-independent systems show a lower accuracy rate compared with speaker-dependent systems, as emotional feature values depend on the speaker and his/her gender. Hence, this paper describes the realization of speaker-independent emotion recognition by rejection using confidence measure to make the emotion recognition system be homogeneous and accurate. From comparison of the proposed methods with conventional method, the improvement and effectiveness of proposed methods were clearly confirmed.

• MALSORI
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• no.60
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• pp.165-180
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• 2006

In this paper, we present several confidence measures (CM) for speech recognition systems to evaluate the reliability of recognition results. We propose heuristic CMs such as mean log-likelihood score, N-best word log-likelihood ratio, likelihood sequence fluctuation and likelihood ratio testing(LRT)-based CMs using several types of anti-models. Furthermore, we propose new algorithms to add weighting terms on phone-level log-likelihood ratio to merge word-level log-likelihood ratios. These weighting terms are computed from the distance between acoustic models and knowledge-based phoneme classifications. LRT-based CMs show better performance than heuristic CMs excessively, and LRT-based CMs using phonetic information show that the relative reduction in equal error rate ranges between $8{\sim}13%$ compared to the baseline LRT-based CMs. We use the support vector machine to fuse several CMs and improve the performance of utterance verification. From our experiments, we know that selection of CMs with low correlation is more effective than CMs with high correlation.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.194-201
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• 2003

In speech recognition, confidence measuring is to decide whether it can be accepted as the recognized results or not. The confidence is measured by integrating frames into phone and word level. In case of word recognition, the confidence measuring verifies the results of recognition and Out-Of-Vocabulary (OOV). Therefore, the post-processing could improve the performance of recognizer without accepting it as a recognition error. In this paper, we measure the confidence modifying log likelihood ratio (LLR) which was the previous confidence measuring. It accumulates only those which the log likelihood ratio is negative when integrating the confidence to phone level from frame level. When comparing the verification performance for the results of word recognizer with the previous method, the FAR (False Acceptance Ratio) is decreased about 3.49% for the OOV and 15.25% for the recognition error when CAR (Correct Acceptance Ratio) is about 90%.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1049-1056
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• 2017

This study focused on the changes in the voices in determining the effects of speech training. This study aimed to make more visible and scientific evaluation of the changes in the voices among the substantial effects obtained from speech training. As a result, some objective differences from before the speech training could be found in the voice of every learner. Each learner showed gradual technical improvement in a variety of vocal elements, including resonance and timbre, accuracy of pronunciation, pause; that is, the voice became more powerful, more accurate pronounced, more pausing and more stable than before the speech training. This study determined if speech training could change a voice and the results are expected to help speech learners participate actively in speech training and see their speech ability improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.13-20
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• 2016

This study explores the effects of an video-recording English speaking task model on learners. The learning model, a form of mobile learning, was developed to facilitate the learners' output practice applying advantages of a smartphone and Text-to Speech. The survey results shows the positive effects of the speaking task on the domain of pronunciation, speaking, listening, writing in terms of students' confidence, as well as general English ability. The study further examines the possibilities and limitations of the speaking task in assisting Korean learners improve their speaking ability, who do not have sufficient exposure to English input or output practice due to the situational limitations where English is learned as a foreign language.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.148-155
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• 2020

This study investigated learners' perceptions of using self-generated listening materials based on Text to Speech. After taking an online training session to learn how to make listening materials for extensive listening practice outside the classroom, the learners were engaged in practice with self-generated listening materials for 10 weeks in a self-directed way. The results show that a majority of the learners found the TTS-based listening materials helpful to reduce anxiety toward listening and enhance self-confidence and motivation, with a positive effect on improving their listening ability. The learners' general satisfaction can be attributed to some beneficial features of TTS-based listening material, including freedom to choose what they want to learn, convenient accessibility to the material, availability of various native speakers' voices, and novelty of digital tools. This suggests that TTS-based digital listening materials can be a useful educational tool to support learners' self-directed listening practice outside the classroom in EFL settings.