• Speech Sciences
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• v.12 no.2
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• pp.17-28
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• 2005

This paper argues that news speech entails a distinct intonational pattern from natural speech, effectively reflecting that it primarily focuses on providing new information. We conducted a phonetic experiment to compare the tonal contours between news speech and natural speech, examining the distributions of pitch accents and the overall pitch ranges. We utilized 70 American Press (AP) radio news utterances and 70 natural utterances extracted from TV dramas. Results show that news speech involves 3.38 H*'s (including L+H* and !H*) within an intonational phrase (IP) or intermediate phrase (ip) whereas natural speech, 1.8 in average. The number of IP/ip's per sentence is 3 in news speech, which is shown in the highest rate of 32.07% of the news speech, but it is merely 1, taking up the highest 41.42% in natural speech. Next, declination tends to be prevented in news speech, and the pitch range is much greater in news speech than in natural speech. Finally, a secondary stress syllable is comparatively frequently given a pitch accent in news speech, explicitly distinct from natural speech. These results can be interpreted as stating that news has the particular purpose of providing new information; every content word tends to be given a H* or its related pitch accent like L+H* or !H* because news speech assumes that every word conveys new information. This definitely brings about more IP/ip's per sentence due to a human physiological constraint; that is, more H*'s will cause more respiratory breaks. Also, greater pitch ranges and pitch accents imposed on secondary stress may be attributed to exaggerating new information.

• MALSORI
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• no.50
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• pp.71-83
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• 2004

Recently, broadcast news speech recognition has become one of the most attractive research areas. If we can transcribe automatically the broadcast news and store their contents in the text form instead of the video or audio signal itself, it will be much easier for us to search for the multimedia databases to obtain what we need. However, the desirable speech signal in the broadcast news are usually affected by the interfering signals such as the background noise and/or the music. Also, the speech of the reporter who is speaking over the telephone or with the ill-conditioned microphone is severely distorted by the channel effect. The interfered or distorted speech may be the main reason for the poor performance in the broadcast news speech recognition. In this paper, we investigated some methods to cope with the problems and we could see some performance improvements in the noisy broadcast news speech recognition.

• Proceedings of the KSPS conference
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• 2006.05a
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• pp.89-93
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• 2006

This paper presents ETRI broadcast news speech recognition system. There are two major issues on the broadcast news speech recognition: 1) real-time processing and 2) out-of-vocabulary handling. For real-time processing, we devised the dual decoder architecture. The input speech signal is segmented based on the long-pause between utterances, and each decoder processes the speech segment alternatively. One decoder can start to recognize the current speech segment without waiting for the other decoder to recognize the previous speech segment completely. Thus, the processing delay is not accumulated. For out-of-vocabulary handling, we updated both the vocabulary and the language model, based on the recent news articles on the internet. By updating the language model as well as the vocabulary, we can improve the performance up to 17.2% ERR.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1E
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• pp.3-11
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• 2002

Broadcast news transcription is one of the hardest tasks in speech recognition because broadcast speech signals have much variability in speech quality, channel and background conditions. We developed a Korean broadcast news speech recognizer. We used a morpheme-based dictionary and a language model to reduce the out-of·vocabulary (OOV) rate. We concatenated the original morpheme pairs of short length or high frequency in order to reduce insertion and deletion errors due to short morphemes. We used a lexicon with multiple pronunciations to reflect inter-morpheme pronunciation variations without severe modification of the search tree. By using the merged morpheme as recognition units, we achieved the OOV rate of 1.7% comparable to European languages with 64k vocabulary. We implemented a hidden Markov model-based recognizer with vocal tract length normalization and online speaker adaptation by maximum likelihood linear regression. Experimental results showed that the recognizer yielded 21.8% morpheme error rate for anchor speech and 31.6% for mostly noisy reporter speech.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2E
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• pp.3-8
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• 2001

In this paper, we will examine a Korean large vocabulary continuous speech recognition (LVCSR) system for broadcast news speech. The combined vowel and implosive unit is included in a phone set together with other short phone units in order to obtain a longer unit acoustic model. The effect of this unit is compared with conventional phone units. The dictionary units for language processing are automatically extracted from eojeols appearing in transcriptions. Triphone models are used for acoustic modeling and a trigram model is used for language modeling. Among three major speaker groups in news broadcasts-anchors, journalists and people (those other than anchors or journalists, who are being interviewed), the speech of anchors and journalists, which has a lot of noise, was used for testing and recognition.

• Speech Sciences
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• v.12 no.1
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• pp.135-142
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• 2005

An important aspect of video indexing is the ability to segment video into meaningful segments, i.e., content-based video segmentation. Since the audio signal in the sound track is synchronized with image sequences in the video program, a speech signal in the sound track can be used to segment video into meaningful segments. In this paper, we propose a new approach to content-based video segmentation. This approach uses closed caption to construct a recognition network for speech recognition. Accurate time information for video segmentation is then obtained from the speech recognition process. For the video segmentation experiment for TV news programs, we made 56 video summaries successfully from 57 TV news stories. It demonstrates that the proposed scheme is very promising for content-based video segmentation.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.147-150
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• 2005

The performance of speech recognition in car environment is severely degraded when there is music or news coming from a radio or a CD player. Since reference signals are available from the audio unit in the car, it is possible to remove them with an adaptive filter. In this paper, we present experimental results of speech recognition in car environment using the echo canceller. For this, we generate test speech signals by adding music or news to the car noisy speech from Aurora2 DB. The HTK-based continuous HMT system is constructed for a recognition system. In addition, the MMSE-STSA method is used to the output of the echo canceller to remove the residual noise more.

• Speech Sciences
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• v.8 no.4
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• pp.59-72
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• 2001

In this paper, we experimented with speaker change detection that uses a statistical method for NOD (News On Demand) service. A specified speaker's change can find out content of each data in speech if analysed because it means change of data contents in news data. Speaker change detection acts as preprocessor that divide input speech by speaker. This is an important preprocessor phase for speaker tracking. We detected speaker change using GLR(generalized likelihood ratio) distance base division and BIC (Bayesian information criterion) base division among matrix method. An experiment verified speaker change point using BIC base division after divide by speaker unit using GLR distance base method first. In the experimental result, FAR (False Alarm Rate) was 63.29 in high noise environment and FAR was 54.28 in low noise environment in MDR (Missed Detection Rate) 15% neighborhood.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.306-312
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• 2022

During the unprecedented COVID-19 outbreak, the public's information needs created an environment where they overwhelmingly consume information on the chronic disease. Given that news media affect the public's emotional well-being, the pandemic situation highlights the importance of paying particular attention to how news stories frame their coverage. In this study, COVID-19 news speech emotion from mainstream broadcasters in South Korea and the United States (US) were analyzed using convolutional neural networks. Results showed that neutrality was detected across broadcasters. However, emotions such as sadness and anger were also detected. This was evident in Korean broadcasters, whereas those emotions were not detected in the US broadcasters. This is the first quantitative vocal emotion analysis of COVID-19 news speech. Overall, our findings provide new insight into news emotion analysis and have broad implications for better understanding of the COVID-19 pandemic.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.87-90
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• 2002

This paper analyzes statistically the relationship between size and balance of text corpus by evaluation of the effect of interview sentences in language model for Korean broadcast news transcription system. Our Korean broadcast news transcription system's ultimate purpose is to recognize not interview speech, but the anchor's and reporter's speech in broadcast news show. But the gathered text corpus for constructing language model consists of interview sentences a portion of the whole, $15\%$ approximately. The characteristic of interview sentence is different from the anchor's and the reporter's in one thing or another. Therefore it disturbs the anchor and reporter oriented language modeling. In this paper, we evaluate the effect of interview sentences in language model for Korean broadcast news transcription system and analyze statistically the relationship between size and balance of text corpus by making an experiment as the same procedure according to varying the size of corpus.