• The Journal of the Acoustical Society of Korea
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• v.29 no.1
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• pp.34-40
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• 2010

As internet spread widely, various service which use the internet have been provided. One of the service is a internet phone. Its usage is increasing by the advantage of cost. But it has a falling off in quality of speech. because it use packet switching method while existing telephone use circuit switching method. Although vocoder use PLC (Packet Loss Concealment) algorithm, it has a weakness of continuous packet loss. In this paper, we propose methods to improve a lowering in quality of speech under continuous loss of packet by using PLC algorithm used in advanced G.729 and G.711. The proposed methods are LP (Linear Prediction) parameter interpolation, excitation signal reconstruction and excitation signal gain reconstruction. As a result, the proposed method shows superior performance about 11%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1590-1609
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• 2021

Laughter is one of the most important nonverbal sound that human generates. It is a means for expressing his emotions. The acoustic and contextual features of this specific sound are different from those of speech and many difficulties arise during their modeling process. During this work, we propose an audio laughter generation system based on unsupervised generative models: the autoencoder (AE) and its variants. This procedure is the association of three main sub-process, (1) the analysis which consist of extracting the log magnitude spectrogram from the laughter database, (2) the generative models training, (3) the synthesis stage which incorporate the involvement of an intermediate mechanism: the vocoder. To improve the synthesis quality, we suggest two hybrid models (LSTM-VAE, GRU-VAE and CNN-VAE) that combine the representation learning capacity of variational autoencoder (VAE) with the temporal modelling ability of a long short-term memory RNN (LSTM) and the CNN ability to learn invariant features. To figure out the performance of our proposed audio laughter generation process, objective evaluation (RMSE) and a perceptual audio quality test (listening test) were conducted. According to these evaluation metrics, we can show that the GRU-VAE outperforms the other VAE models.

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.37-46
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• 2001

This paper proposes a new noise suppression method using the Wavelet transform analysis. The noise suppressor using the Wavelet transform shows the more effective advantages in a babble noise than one using the short-time Fourier transform. We designed a new channel structure based on spectral subtraction of Wavelet transform coefficients and used the Wavelet mask pattern with more higher time resolution in high frequency. It showed a good adaptation capability for babble noise with a non-stationary property. To evaluate the performance of proposed noise canceller, the informal subjective listening tests (Mos tests) were performed in background noise environments (car noise, street noise, babble noise) of mobile communication. The proposed noise suppression algorithm showed about MOS 0.2 performance improvements than the suppression algorithm of EVRC in informal listening tests. The noise reduction by the proposed method was shown in spectrogram of speech signal.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.71-83
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• 2003

In this paper, a novel transcoding algorithm for the G.729A and the Selectable Mode Vocoder(SMV) vocoders via direct parameter transformation is proposed. In contrast to the conventional tandem transcoding algorithm, the proposed algorithm converts the parameters of one coder to the other without going through the decoding and encoding processes. In transcoder from SMV to G.729A, LSP conversion algorithm, pitch delay conversion algorithm and transcoding algorithm in lower rate are proposed, and in transcoder from G.729A to SMV, LSP conversion algorithm, pitch delay conversion algorithm and rate selection algorithm are proposed. Evaluation results show that while exhibiting better computational and delay characteristics, the proposed algorithm produces equivalent or Improved speech quality to that produced by the tandem transcoding algorithm.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.9-13
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• 2023

In this study, we explored the potential of integrating interactive AI callbot technology into the medical consultation domain as part of a broader service development initiative. Aimed at enhancing patient satisfaction, the AI callbot was designed to efficiently address queries from hospitals' primary users, especially the elderly and those using phone services. By incorporating an AI-driven callbot into the hospital's customer service center, routine tasks such as appointment modifications and cancellations were efficiently managed by the AI Callbot Agent. On the other hand, tasks requiring more detailed attention or specialization were addressed by Human Agents, ensuring a balanced and collaborative approach. The deep learning model for voice recognition for this study was based on the Transformer model and fine-tuned to fit the medical field using a pre-trained model. Existing recording files were converted into learning data to perform SSL(self-supervised learning) Model was implemented. The ANN (Artificial neural network) neural network model was used to analyze voice signals and interpret them as text, and after actual application, the intent was enriched through reinforcement learning to continuously improve accuracy. In the case of TTS(Text To Speech), the Transformer model was applied to Text Analysis, Acoustic model, and Vocoder, and Google's Natural Language API was applied to recognize intent. As the research progresses, there are challenges to solve, such as interconnection issues between various EMR providers, problems with doctor's time slots, problems with two or more hospital appointments, and problems with patient use. However, there are specialized problems that are easy to make reservations. Implementation of the callbot service in hospitals appears to be applicable immediately.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.149-156
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• 2012

Support vector machines (SVMs) are well known for their pattern recognition capability, but proper care should be taken to alleviate their inherent implementation cost resulting from high computational intensity and memory requirement, especially in embedded systems where only limited resources are available. Since the memory requirement determined by the dimensionality and the number of support vectors is generally too high for a cache in embedded systems to accomodate, frequent accesses to the main memory occur inevitably whenever the cache is not able to provide requested data to the processor. These frequent accesses to the main memory result in overall performance degradation and increased energy consumption because a memory access typically takes longer and consumes more energy than a cache access or a register access. In this paper, we propose a technique that reduces the number of main memory accesses by optimizing the data access pattern of the SVM-based classifier in such a way that the temporal locality of the accesses increases, fully utilizing data loaded into the processor chip. With experiments, we confirm the enhancement made by the proposed technique in terms of the number of memory accesses, overall execution time, and energy consumption.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2027-2035
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• 2001

In this paper, we present an audio stream delivery using the AMR (Adaptive Multi-Rate) coder that was adopted by ETSI and 3GPP as a standard vocoder for next generation IMT-2000 service in which includes combined sender (FEC) and receiver reconstruction technique in the Internet. By use of the media-specific FEC scheme, the possibility to recover lost packets can be much increased due to the addition of repair data to a main data stream, by which the contents of lost packets can be recovered. The AMR codec is based on the code-excited linear predictive (CELP) coding model. So we use a frame erasure concealment for CELP-based coders. The proposed scheme is evaluated with ITU-T G.729 (CS-ACELP) coder and AMR - 12.2 kbit/s through the SNR (Signal to Noise Ratio) and the MOS (Mean Opinion Score) test. The proposed scheme provides 1.1 higher in Mean Opinion Score value and 5.61 dB higher than AMR - 12.2 kbit/s in terms of SNR in 10% packet loss, and maintains the communicab1e quality speech at frame erasure rates lop to 20%.